Microbial microfluidic biosensor

ABSTRACT

Provided are a microfluidic biosensors that are suitable for continuously monitoring toxin levels in water supplies.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/551,124, filed on Jan. 26, 2018, which is a National Stageapplication under 35 U.S.C. § 371 of International No.PCT/US2016/017889, filed Feb. 12, 2016, which claims priority to U.S.Provisional Application Ser. No. 62/116,888, entitled “MicrobialMicrofluidic Biosensor” filed Feb. 16, 2015. The entire contents of theforegoing are hereby incorporated by reference.

STATEMENT REGARDING FEDERALLY SPONSORED R&D

This invention was made with government support under W911NF-14-2-0032,awarded by the United States Army. The government has certain rights inthe invention.

REFERENCE TO SEQUENCE LISTING, TABLE, OR COMPUTER PROGRAM LISTING

The present application is being filed along with a Sequence Listing inelectronic format. The Sequence Listing is provided as a file entitled15670-0291002—Sequence Listing.txt created Aug. 31, 2021, which is 54.4kb in size. The information is the electronic format of the SequenceListing is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

Provided are microfluidic biosensors that are suitable for continuouslymonitoring analyte levels.

BACKGROUND OF THE INVENTION

The existing art consists of using engineered biosensor strains in awell plate to test for water toxin levels. Alternatively, electronictesting devices not based on bacterial biosensors provide disposabletest strips or cartridges to perform an individual test for a toxin ofinterest. Neither of these methods offer a cost-effective option forcontinuous monitoring of water toxin levels without human intervention.

SUMMARY OF THE INVENTION

In a first aspect, a microfluidic device comprising one or more coloniesor cultures of microorganism cells at one or more predeterminedaddressable locations is provided, wherein each of the cells within theone or more colonies or cultures comprises an expression cassettecomprising a biosensor or promoter operably linked to a polynucleotideencoding a detectable agent, wherein transcription of the biosensor orpromoter is modulated by the presence of an analyte. In someembodiments, the detectable agent is a nucleic acid, detectable protein,antibody-linked reporter protein, enzymatic assay product, orelectrochemical reaction product. In some embodiments, the detectableprotein comprises an activity that is increased or decreased in thepresence of an analyte. In some embodiments, the detectable agent is adetectable protein, wherein the detectable protein provides a detectablesignal. In some embodiments, the detectable protein is a fluorescentprotein or a luminescent protein. In some embodiments, the detectableagent is a detectable protein, wherein the detectable protein provides adetectable signal. In some embodiments, the nucleic acid is RNA or DNA.In some embodiments, the microfluidic device comprises microfluidicchannels or lumens arranged in a rotationally symmetric gill celltrapping configuration. In some embodiments, the microfluidic channelsor lumens are arranged in 16 or 18 rotationally symmetric gills. In someembodiments, the device comprises about 20,000 chambers or gill traps.In some embodiments, transcription of the biosensor or promoter isinduced, promoted or increased by the presence of an analyte. In someembodiments, transcription of the biosensor or promoter is induced,promoted or increased by the presence of an analyte selected from thegroup consisting of arsenic, cadmium, chromium VI, cobalt, copper, lead,malathion, mercury and zinc. In some embodiments, the biosensor orpromoter is selected from the group consisting of ParsR (arsenic), PcadC(cadmium), PcadR (cadmium), PzntA (cadmium), PchrB (chromium VI), PchrS(chromium VI), PrecN (chromium VI), PsulA (chromium VI), PumuD (chromiumVI), PdadA (cobalt), Phmp (cobalt), PilvB (cobalt), PilvB (cobalt),PlipA (cobalt), PmmuP (cobalt), PnmtR (cobalt), PsoxR (cobalt), PtehA(cobalt), PygbA (cobalt), PyjbJ (cobalt), PyqfA (cobalt), PcopA(copper), PcusC (copper), PcusR (copper), PpbrR (lead), PmntH (lead),PshiA (lead), PybiI (lead), PyjjZ (lead), PcusC (malathion), PnemR(malathion), PmerR (mercury), PmntH (zinc), PshiA (zinc), PyjjZ (zinc),PzntA (zinc) and PzraP (zinc). In some embodiments, the biosensor orpromoter comprises a polynucleotide having a sequence identity of atleast about 90% to a polynucleotide sequence selected from the groupconsisting of SEQ ID NOs: 1-43. In some embodiments, the biosensor orpromoter comprises a polynucleotide having a sequence identity of atleast about 90% to a polynucleotide sequence selected from the groupconsisting of SEQ ID NOs: 2, 5, 8, 11, 12, 13, 14, 15, 16, 17, 20, 23,25, 28, 29, 30 and 33. In some embodiments, transcription of thebiosensor or promoter is decreased or inhibited by the presence of ananalyte. In some embodiments, the biosensor or promoter is decreased orinhibited by the presence of ammonia. In some embodiments, the biosensoror promoter which is decreased or inhibited by the presence of ammoniais selected from the group consisting of PnasA (ammonia), PnasB(ammonia), Pspo1-tnrA1 (ammonia) and Pspo1-tnrA2 (ammonia). In someembodiments, the biosensor or promoter comprises a polynucleotidesequence having at least about 90% sequence identity to SEQ ID NO:1. Insome embodiments, the device detects or monitors the presence or levelsof one or more analytes at the following concentrations: a) at leastabout 0.2 nM arsenic; b) at least about 0.44 μM cadmium; c) at leastabout 2.5 μM chromium(VI); d) at least about 5 μM copper; e) at leastabout 1 μM mercury; f) at least about 1.8 μM lead; g) at least about72.5 mg/l malathion; and/or h) at least about 1 ppm ammonia. In someembodiments, the microorganism cells are selected from the groupconsisting of bacteria, cyanobacteria, microalgae and fungi. In someembodiments, the microorganism cells comprise a bacteria selected fromthe group consisting of Escherichia coli, Bacillus subtilis, Salmonellasp., Aliivibrio fischeri, Pseudomonas fluorescens, Bacillus sp.,Cupriavidus metallidurans, Deinococcus radiodurans, and Staphylococcusaureus. In some embodiments, the microorganism cells comprise a fungusselected from the group consisting of Saccharomyces cerevisiae andTrichosporon cutaneum. In some embodiments, the microorganism cellscomprise Synechocystis sp. In some embodiments, the device is capable ofculturing at least about 4,000 individual strains of microorganismcells. In some embodiments, the expression cassette is in a plasmidwhich has been introduced into the microorganism. In some embodiments,the expression cassette is integrated into the genome of themicroorganism. In some embodiments, the one or more colonies or culturesof microorganisms are lyophilized (freeze-dried). In some embodiments,the one or more colonies or cultures of microorganisms are one or moredifferent species. In some embodiments, the one or more colonies orcultures of microorganisms are the same species. In some embodiments,the detectable protein is a fluorescent protein. In some embodiments,the fluorescent protein is selected from the group consisting of greenfluorescent protein, a yellow fluorescent protein, a cyan fluorescentprotein, a red-shifted green fluorescent protein (rs-GFP), and miniSOG.In some embodiments, the detectable protein is a luminescent protein. Insome embodiments, the luminescent protein is bacterial luciferase (Lux).In some embodiments, said microfluidic device comprises a plurality ofsaid colonies or cultures and wherein each of said plurality of coloniesor cultures comprises an expression cassette comprising a biosensor orpromoter operably linked to a polynucleotide encoding a detectableprotein wherein transcription of the biosensor or promoter is modulatedby the presence of a different analyte than the biosensor or promoter inthe other of said plurality of colonies or cultures. In someembodiments, the plurality of colonies or cultures comprises at least 2colonies or cultures, 3 colonies or cultures, 4 colonies or cultures, 5colonies or cultures, 6 colonies or cultures or 7 colonies or cultures.In some embodiments, the colonies or cultures comprise microorganismcells are selected from the group consisting of bacteria, cyanobacteria,microalgae and fungi. In some embodiments, the transcription of thebiosensor or promoter is induced, promoted or increased by the presenceof an analyte selected from the group consisting of arsenic, cadmium,chromium VI, cobalt, copper, lead, malathion, mercury and zinc. In someembodiments, the biosensor or promoter is selected from the groupconsisting of ParsR (arsenic), PcadC (cadmium), PcadR (cadmium), PzntA(cadmium), PchrB (chromium VI), PchrS (chromium VI), PrecN (chromiumVI), PsulA (chromium VI), PumuD (chromium VI), PdadA (cobalt), Phmp(cobalt), PilvB (cobalt), PilvB (cobalt), PlipA (cobalt), PmmuP(cobalt), PnmtR (cobalt), PsoxR (cobalt), PtehA (cobalt), PygbA(cobalt), PyjbJ (cobalt), PyqfA (cobalt), PcopA (copper), PcusC(copper), PcusR (copper), PpbrR (lead), PmntH (lead), PshiA (lead),PybiI (lead), PyjjZ (lead), PcusC (malathion), PnemR (malathion), PmerR(mercury), PmntH (zinc), PshiA (zinc), PyjjZ (zinc), PzntA (zinc) andPzraP (zinc)

In a second aspect, a system comprising the microfluidic device of anyone of the embodiments, is provided. In some embodiments, the systemfurther comprises a housing enclosing the device, comprising within thehousing: i) a peristaltic pump in fluid communication with themicrofluidic device; ii) a fluorescent or luminescent signal sensor ordetector comprising a platform to accommodate the microfluidic device;and iii) electronics for acquiring and processing data in electroniccommunication with the fluorescent or luminescent signal sensor ordetector. In some embodiments, the system is configured as depicted inFIG. 7. In some embodiments, the housing is temperature and/or humiditycontrolled.

In a third aspect, a method of detecting the presence or levels of ananalyte in an aqueous sample is provided, wherein the method comprisesa) inputting into the microfluidic lumens of a microfluidic device ofany one of the embodiments provided herein an aqueous test samplesuspected of comprising one or more analytes of interest such that theaqueous test sample contacts the one or more colonies or cultures ofmicroorganism cells; b) measuring the amount of a detectable agent thatcan correspond to a quantifiable level of analyte. In some embodiments,the detectable protein is a fluorescent protein or a luminescentprotein. In some embodiments, measuring comprises measuring thetranscription and/or activation levels of the detectable agent, whereinthe transcription and/or activation levels of the detectable proteinexpressed by the one or more colonies or cultures at the predeterminedaddressable locations correspond to a quantifiable level of analyte. Insome embodiments, the method further comprises measuring thefluorescence and/or the luminescence of the one or more detectableproteins expressed by the one or more colonies or cultures at thepredetermined addressable locations within the device.

In a fourth aspect a collection comprising a plurality of differentnucleic acids is provided, wherein each nucleic acid within saidcollection comprises a first sequence comprising a promoter responsiveto an analyte different from the analyte to which the other promoters inthe other nucleic acids are responsive; and a second sequence comprisinga reporter protein. In some embodiments, the promoter is selected fromthe group consisting of ParsR (arsenic), PcadC (cadmium), PcadR(cadmium), PzntA (cadmium), PchrB (chromium VI), PchrS (chromium VI),PrecN (chromium VI), PsulA (chromium VI), PumuD (chromium VI), PdadA(cobalt), Phmp (cobalt), PilvB (cobalt), PilvB (cobalt), PlipA (cobalt),PmmuP (cobalt), PnmtR (cobalt), PsoxR (cobalt), PtehA (cobalt), PygbA(cobalt), PyjbJ (cobalt), PyqfA (cobalt), PcopA (copper), PcusC(copper), PcusR (copper), PpbrR (lead), PmntH (lead), PshiA (lead),PybiI (lead), PyjjZ (lead), PcusC (malathion), PnemR (malathion), PmerR(mercury), PmntH (zinc), PshiA (zinc), PyjjZ (zinc), PzntA (zinc) andPzraP (zinc). In some embodiments, the reporter protein is a fluorescentprotein. In some embodiments, the fluorescent protein is selected fromthe group consisting of green fluorescent protein, a yellow fluorescentprotein, a cyan fluorescent protein, a red-shifted green fluorescentprotein (rs-GFP), and miniSOG.

In a fifth aspect, a method of making a plurality of cell strains forthe detection of an analyte, the method comprising: introducing into aplurality of cell strains the collection of anyone of the embodimentsprovided herein.

In a sixth aspect, cell strains for the detection of an analyte isprovided, wherein the cell strains comprises the nucleic acid of anyoneof the embodiments provided herein or made by the method of any of theembodiments provided herein. In some embodiments, the cell is ofbacteria, cyanobacteria, microalgae and fungi. In some embodiments, thebacteria is selected from the group consisting of Escherichia coli,Bacillus subtilis, Salmonella sp., Aliivibrio fischeri, Pseudomonasfluorescens, Bacillus sp., Cupriavidus metallidurans, Deinococcusradiodurans, and Staphylococcus aureus. In some embodiments, the cell isa fungus selected from the group consisting of Saccharomyces cerevisiaeand Trichosporon cutaneum. In some embodiments, the cell comprisesSynechocystis sp.

In a seventh aspect, a microfluidic device comprising a plurality oflyophilized cell strains wherein each of said plurality of lyophilizedcells strains has been genetically engineered to produce an increased ordecreased amount of a detectable agent in the presence of an analyterelative to the amount produced in the absence of said analyte. In someembodiments, the detectable agent is a nucleic acid, detectable protein,antibody-linked reporter protein, enzymatic assay product, orelectrochemical reaction product. In some embodiments, the detectableprotein is a fluorescent protein or a luminescent protein. In someembodiments, the detectable protein comprises an activity that isincreased or decreased in the presence of an analyte. In someembodiments, the detectable agent is a detectable protein, wherein thedetectable protein provides a detectable signal. In some embodiments,the nucleic acid is RNA or DNA. In some embodiments, the microfluidicdevice comprises microfluidic channels or lumens arranged in arotationally symmetric gill cell trapping configuration. In someembodiments, the microfluidic channels or lumens are arranged in 16 or18 rotationally symmetric gills. In some embodiments, the devicecomprises about 20,000 chambers or gill traps. In some embodiments,transcription of the biosensor or promoter is induced, promoted orincreased by the presence of an analyte. In some embodiments,transcription of the biosensor or promoter is induced, promoted orincreased by the presence of an analyte selected from the groupconsisting of arsenic, cadmium, chromium VI, cobalt, copper, lead,malathion, mercury and zinc. In some embodiments, the biosensor orpromoter is selected from the group consisting of ParsR (arsenic), PcadC(cadmium), PcadR (cadmium), PzntA (cadmium), PchrB (chromium VI), PchrS(chromium VI), PrecN (chromium VI), PsulA (chromium VI), PumuD (chromiumVI), PdadA (cobalt), Phmp (cobalt), PilvB (cobalt), PilvB (cobalt),PlipA (cobalt), PmmuP (cobalt), PnmtR (cobalt), PsoxR (cobalt), PtehA(cobalt), PygbA (cobalt), PyjbJ (cobalt), PyqfA (cobalt), PcopA(copper), PcusC (copper), PcusR (copper), PpbrR (lead), PmntH (lead),PshiA (lead), PybiI (lead), PyjjZ (lead), PcusC (malathion), PnemR(malathion), PmerR (mercury), PmntH (zinc), PshiA (zinc), PyjjZ (zinc),PzntA (zinc) and PzraP (zinc). In some embodiments, the biosensor orpromoter comprises a polynucleotide having a sequence identity of atleast about 90% to a polynucleotide sequence selected from the groupconsisting of SEQ ID NOs: 1-43. In some embodiments, the biosensor orpromoter comprises a polynucleotide having a sequence identity of atleast about 90% to a polynucleotide sequence selected from the groupconsisting of SEQ ID NOs: 2, 5, 8, 11, 12, 13, 14, 15, 16, 17, 20, 23,25, 28, 29, 30 and 33. In some embodiments, transcription of thebiosensor or promoter is decreased or inhibited by the presence of ananalyte. In some embodiments, the biosensor or promoter is decreased orinhibited by the presence of ammonia. In some embodiments, the biosensoror promoter which is decreased or inhibited by the presence of ammoniais selected from the group consisting of PnasA (ammonia), PnasB(ammonia), Pspo1-tnrA1 (ammonia) and Pspo1-tnrA2 (ammonia). In someembodiments, the biosensor or promoter comprises a polynucleotidesequence having at least about 90% sequence identity to SEQ ID NO:1. Insome embodiments, the device detects or monitors the presence or levelsof one or more analytes at the following concentrations: a) at leastabout 0.2 nM arsenic; b) at least about 0.44 μM cadmium; c) at leastabout 2.5 μM chromium(VI); d) at least about 5 μM copper; e) at leastabout 1 μM mercury; f) at least about 1.8 μM lead; g) at least about72.5 mg/l malathion; and/or h) at least about 1 ppm ammonia. In someembodiments, the microorganism cells are selected from the groupconsisting of bacteria, cyanobacteria, microalgae and fungi. In someembodiments, the microorganism cells comprise a bacteria selected fromthe group consisting of Escherichia coli, Bacillus subtilis, Salmonellasp., Aliivibrio fischeri, Pseudomonas fluorescens, Bacillus sp.,Cupriavidus metallidurans, Deinococcus radiodurans, and Staphylococcusaureus. In some embodiments, the microorganism cells comprise a fungusselected from the group consisting of Saccharomyces cerevisiae andTrichosporon cutaneum. In some embodiments, the microorganism cellscomprise Synechocystis sp. In some embodiments, the device is capable ofculturing at least about 4,000 individual strains of microorganismcells. In some embodiments, the expression cassette is in a plasmidwhich has been introduced into the microorganism. In some embodiments,the expression cassette is integrated into the genome of themicroorganism. In some embodiments, the one or more colonies or culturesof microorganisms are one or more different species. In someembodiments, the one or more colonies or cultures of microorganisms arethe same species. In some embodiments, the detectable protein is afluorescent protein. In some embodiments, the fluorescent protein isselected from the group consisting of green fluorescent protein, ayellow fluorescent protein, a cyan fluorescent protein, a red-shiftedgreen fluorescent protein (rs-GFP), and miniSOG. In some embodiments,the detectable protein is a luminescent protein. In some embodiments,the luminescent protein is bacterial luciferase (Lux).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1, (FIGS. 1A, 1B, 1C, 1D, 1E, 1F, 1G, 1H, and 1I) illustratessensitive and specific gene candidates for eight toxins identified byRNA-Seq analysis in E. coli MG1655, where the mean fold change thresholdto indicate DE has been set to 2.5. The eight toxins are arsenic (arsR),cadmium (zntA), chromium (VI) (recN), chromium (VI) (sulA), cobalt(ygbA), copper (curR), lead (ybil), malathion (nemR) and zinc (araP).

FIG. 2 illustrates that the nasB promoter in Bacillus subtilis 168 wasfound to be sensitive and specifically downregulated in response toammonia. As shown in the two panels below, the numbers at the x-axiscorrespond to the four corresponding bars above the numbers. For examplethe first 4 bars correspond to the number 1, the second set of 4 barscorrespond to the number 2 and so forth.

FIG. 3, (FIGS. 3A, 3B, 3C, 3D, 3E, 3F, 3G, and 3H), illustrate on-chiptime-lapse induction responses of promoter constructs. All sensorconstructs are on plasmids transformed into E. coli MG1655, except forthe ammonia construct which is integrated into the genome of B. subtilis168. 3A) pRS18 and pZA47a show induction by arsenic. 3B) Cd1 showsinduction by cadmium. 3C) Cr11 shows induction by chromium(VI). 3D) Cu1shows induction by copper. 3E) Hg3 shows induction by mercury. 3F) Pb7shows induction by lead. 3G) Mall shows induction by malathion. 3H) Amm3shows induction by ammonia.

FIG. 4 illustrates Cryoprotectant revival rates for arsenic detecting E.coli plasmid/strain pLB-As3/MG1655. The cryoprotectant is listed overeach plot, with the overnight and revival media listed in that order inparentheses; i.e., a strain cryoprotected in LB after being grownovernight in M9 and revived in Trace Select M9 media is denoted by LB(M9/Trace).

FIG. 5, (FIGS. 5A, 5B) 5A) Chip design with 16 loading ports (16 teardrop shaped constructions) and reservoirs. 5B) Resulting chip withindependently cryoprotected, loaded, and lyophilized strains.

FIG. 6 (FIGS. 6A, 6B and 6C) illustrates a comparison of optical systemsusing the chromium Cr11 sensor strain induced with 1.25 uM of chromate.The same microfluidic device was imaged with a) and b) low costbiosensor optics developed by the Ziva Corporation and c) a researchgrade Olympus IX81 microscope. 6A) and 6B) show brightfield and GFPfluorescence images acquired by the Ziva optics, magnified to match theimage field of view produced by the Olympus optics with 4× objective.(Note that this decreases the apparent resolution of the Ziva optics.)6C) shows the same fluorescence image as in b), but acquired using theOlympus optics with 4× objective. The optical quality of the Ziva systemis remarkable given the difference in cost ($2K for Ziva versus about$100K for Olympus).

FIG. 7, (FIGS. 7A, 7B, 7C, 7D, 7E, 7F, 7G), illustrates biosensorself-contained prototype: 7A) Device enclosure, note temperaturecontroller (upper left) and peristaltic pump (lower right) mounted tothe aluminum front panel. 7B) Enclosure with front panel opened toexpose internal components. b1: Electronics sub-enclosure, b2:temperature controller, b3: AC power distribution devices, b4:tri-output DC power supply, b5: Ziva optical assembly, b6: Fan/heater,b7: DC power distribution block, b8: peristaltic pump. 7C) Solidworksdetail of the electronics sub-enclosure. c1: Arduino Uno, c2: BuckBlockLED drivers, c3: PandaBoard, c4: LED control relays. 7D) Ziva opticalassembly. d1: transmitted light optics, d2: stage/temperature probe, d3:Focal adjustment knob, d4: dichroic mirror holder, d5: GFP excitationoptics, d6: monochrome camera. 7E) Microfluidic device being illuminatedwith the GFP excitation LED. 7F). Representative images acquired withour device prototype—top: transmitted light, bottom: GFP excitation. 7G)image of the prototype running in an outdoor environment with solarpower.

FIG. 8, (FIGS. 8A, 8B, 8C, 8D, 8E, 8F, 8G, 8H), illustrates ReceiverOperating Characteristic (ROC) curves for the sensors. This parametrizedcurve quantifies the trade-off between the true positive rate and thefalse positive rate of a classifier. An optimal classifier isrepresented by a step function (e.g. Arsenic), since this pointcorresponds to correct identification of 100% of the positive cases(toxin present) with no false alarms. In the other limit, worst-caseclassifiers are characterized by a diagonal ROC curve. Note that thearsenic, chromium, copper, lead and malathion sensors are nearly perfectat all tested levels, while very low levels of cadmium, mercury, andammonia lead to suboptimal classification due to the detection limit ofthe genetic circuitry.

FIG. 9 illustrates two platforms with wireless capability: (Left) theSitara-ARM (Texas Instruments AM335x), and (Right) the PandaBoard usingthe Cortex-A9 processor. The PandaBoard has allowed us to install a fullLinux operating system.

FIG. 10, (FIGS. 10A and 10B) illustrate 10A) Brightfield image of amicrofluidic device comprising sixteen rotationally symmetric “gill”cell trapping regions for imaging sixteen biosensor strains. 10B)Fluorescence image of the same microfluidic device. Each cell straintrapped in a “gill” region is engineered to produce GFP upon exposure toa specific toxin in the mixed medium. The fluorescence image is analyzedto interpret the intensity of the GFP signal in each trapping region asa concentration of the relevant toxin in the natural water source.

FIG. 11, (FIGS. 11A, 11B, 11C and 11D) illustrate Quadratic ProgrammingFeature Selection weights representing the importance of each pixel forthe discrimination task (0: least relevant pixels/1: most relevantpixels) a) Weights assigned to each pixel. Weights of the first b) 100,c) 200, and d) 500 features selected.

FIG. 12 illustrates a side-by-side comparison of reporter systems onotherwise identical plasmid backbones demonstrates the superiordetection limit achieved by using luminescence.

FIG. 13 shows the response of the zntA promoter in E. coli MG1655 forsensing cadmium and zinc. The zntA promoter responds monotonically toincreasing concentrations of cadmium alone and in combination with otherheavy metals (rightmost conditions) without exhibiting crosstalk. Acadmium-specific sensor can be implemented by combining this responsewith the zinc-specific response of zraP using the Boolean expression(zntA){circumflex over ( )}(¬zraP). As shown in the two panels below,the numbers at the x-axis correspond to the four corresponding barsabove the numbers. For example the first 4 bars correspond to the number1, the second set of 4 bars correspond to the number 2 and so forth.

FIG. 14 shows the response of the recN promoter in E. coli MG1655 forspecifically sensing chromium(VI). The response of the recN promoter isspecific to chromium(VI) alone but only shows sensitivity to highconcentrations of chromium(VI). As shown in the two panels below, thenumbers at the x-axis correspond to the four corresponding bars abovethe numbers. For example the first 4 bars correspond to the number 1,the second set of 4 bars correspond to the number 2 and so forth.

FIG. 15 shows the response of the sulA promoter in E. coli MG1655 forspecifically sensing chromium(VI). The response of the sulA promoter isspecific to chromium(VI) alone but only shows sensitivity to highconcentrations of chromium(VI). As shown in the two panels below, thenumbers at the x-axis correspond to the four corresponding bars abovethe numbers. For example the first 4 bars correspond to the number 1,the second set of 4 bars correspond to the number 2 and so forth.

FIG. 16 shows the response of the umuD promoter in E. coli MG1655 forspecifically sensing chromium(VI). The response of the umuD promoter isspecific to chromium(VI) alone but only shows sensitivity to highconcentrations of chromium(VI). As shown in the two panels below, thenumbers at the x-axis correspond to the four corresponding bars abovethe numbers. For example the first 4 bars correspond to the number 1,the second set of 4 bars correspond to the number 2 and so forth.

FIG. 17 shows the response of the dadA promoter in E. coli MG1655 forspecifically sensing cobalt. The response of the dadA promoter isspecific to cobalt alone. As shown in the two panels below, the numbersat the x-axis correspond to the four corresponding bars above thenumbers. For example the first 4 bars correspond to the number 1, thesecond set of 4 bars correspond to the number 2 and so forth.

FIG. 18 shows the response of the hmp promoter in E. coli MG1655 forspecifically sensing cobalt. The response of the hmp promoter isspecific to cobalt alone. As shown in the two panels below, the numbersat the x-axis correspond to the four corresponding bars above thenumbers. For example the first 4 bars correspond to the number 1, thesecond set of 4 bars correspond to the number 2 and so forth.

FIG. 19 shows the response of the ilvB promoter in E. coli MG1655 forspecifically sensing cobalt. The response of the ilvB promoter isspecific to cobalt alone. As shown in the two panels below, the numbersat the x-axis correspond to the four corresponding bars above thenumbers. For example the first 4 bars correspond to the number 1, thesecond set of 4 bars correspond to the number 2 and so forth.

FIG. 20 shows the response of the lipA promoter in E. coli MG1655 forspecifically sensing cobalt. The response of the lipA promoter isspecific to cobalt alone. As shown in the two panels below, the numbersat the x-axis correspond to the four corresponding bars above thenumbers. For example the first 4 bars correspond to the number 1, thesecond set of 4 bars correspond to the number 2 and so forth.

FIG. 21 shows the response of mmuP promoter in E. coli MG1655 forspecifically sensing cobalt. The response of the mmuP promoter isspecific to cobalt alone. As shown in the two panels below, the numbersat the x-axis correspond to the four corresponding bars above thenumbers. For example the first 4 bars correspond to the number 1, thesecond set of 4 bars correspond to the number 2 and so forth.

FIG. 22 shows the response of the soxR promoter in E. coli MG1655 forspecifically sensing cobalt. The response of the soxR promoter isspecific to cobalt alone. The response of the soxR promoter is specificto cobalt alone. As shown in the two panels below, the numbers at thex-axis correspond to the four corresponding bars above the numbers. Forexample the first 4 bars correspond to the number 1, the second set of 4bars correspond to the number 2 and so forth.

FIG. 23 shows the response of the tehA promoter in E. coli MG1655 forspecifically sensing cobalt. The response of the tehA promoter isspecific to cobalt alone. As shown in the two panels below, the numbersat the x-axis correspond to the four corresponding bars above thenumbers. For example the first 4 bars correspond to the number 1, thesecond set of 4 bars correspond to the number 2 and so forth.

FIG. 24 shows the response of the ygbA promoter in E. coli MG1655 forspecifically sensing cobalt. The response of the ygbA promoter isspecific to cobalt alone. As shown in the two panels below, the numbersat the x-axis correspond to the four corresponding bars above thenumbers. For example the first 4 bars correspond to the number 1, thesecond set of 4 bars correspond to the number 2 and so forth.

FIG. 25 shows the response of the yjbJ promoter in E. coli MG1655 forspecifically sensing cobalt. The response of the yjbJ promoter isspecific to cobalt alone. As shown in the two panels below, the numbersat the x-axis correspond to the four corresponding bars above thenumbers. For example the first 4 bars correspond to the number 1, thesecond set of 4 bars correspond to the number 2 and so forth.

FIG. 26 shows the response of the yqfA promoter in E. coli MG1655 forspecifically sensing cobalt. The response of the yqfA promoter isspecific to cobalt alone. As shown in the two panels below, the numbersat the x-axis correspond to the four corresponding bars above thenumbers. For example the first 4 bars correspond to the number 1, thesecond set of 4 bars correspond to the number 2 and so forth.

FIG. 27 shows the response of the cusC promoter in E. coli MG1655 forsensing copper and malathion. The cusC promoter responds monotonicallyto increasing concentrations of copper alone and to low concentrationsof malathion alone. A copper-specific sensor can be implemented bycombining this response with the malathion-specific response of nemRusing the boolean expression (cusC){circumflex over ( )}(¬nemR).Alternatively, a malathion-specific sensor can be implemented bycombining this response with the copper-specific response of cusR usingthe boolean expression (cusC){circumflex over ( )}(¬cusR). As shown inthe two panels below, the numbers at the x-axis correspond to the fourcorresponding bars above the numbers. For example the first 4 barscorrespond to the number 1, the second set of 4 bars correspond to thenumber 2 and so forth.

FIG. 28 shows the response of the cusR promoter in E. coli MG1655 forspecifically sensing copper. The response of the cusR promoter isspecific to copper alone but only shows sensitivity to highconcentrations of copper. As shown in the two panels below, the numbersat the x-axis correspond to the four corresponding bars above thenumbers. For example the first 4 bars correspond to the number 1, thesecond set of 4 bars correspond to the number 2 and so forth.

FIG. 29 shows the response of the mntH promoter in E. coli MG1655 forsensing lead and zinc. The mntH promoter responds to high concentrationsof lead alone and high concentrations of zinc alone. A lead specificsensor can be implemented by combining this response with thezinc-specific response of zraP using the boolean expression(mntH){circumflex over ( )}(¬zraP). Alternatively, a zinc-specificsensor can be implemented by combining this response with thelead-specific response of ybiI using the boolean expression(mntH){circumflex over ( )}(¬ybiI). As shown in the two panels below,the numbers at the x-axis correspond to the four corresponding barsabove the numbers. For example the first 4 bars correspond to the number1, the second set of 4 bars correspond to the number 2 and so forth.

FIG. 30 shows the response of the shiA promoter in E. coli MG1655 forsensing lead and zinc. The shiA promoter responds to high concentrationsof lead alone and high concentrations of zinc alone. A lead specificsensor can be implemented by combining this response with thezinc-specific response of zraP using the boolean expression(shiA){circumflex over ( )}(¬zraP). Alternatively, a zinc-specificsensor can be implemented by combining this response with thelead-specific response of ybiI using the boolean expression(shiA){circumflex over ( )}(¬ybiI). As shown in the two panels below,the numbers at the x-axis correspond to the four corresponding barsabove the numbers. For example the first 4 bars correspond to the number1, the second set of 4 bars correspond to the number 2 and so forth.

FIG. 31 shows the response of the ybiI promoter in E. coli MG1655 forspecifically sensing lead. The ybiI promoter responds specifically andmonotonically to increasing concentrations of lead alone. As shown inthe two panels below, the numbers at the x-axis correspond to the fourcorresponding bars above the numbers. For example the first 4 barscorrespond to the number 1, the second set of 4 bars correspond to thenumber 2 and so forth.

FIG. 32 shows the response of the yjjZ promoter in E. coli MG1655 forsensing lead and zinc. the yjjZ promoter responds monotonically toincreasing concentrations of zinc alone and to high concentrations oflead alone. A lead-specific sensor can be implemented by combining thisresponse with the zinc-specific response of zraP using the booleanexpression (yjjZ){circumflex over ( )}(¬zraP). Alternatively, azinc-specific sensor can be implemented by combining this response withthe lead-specific response of ybiI using the boolean expression(yjjZ){circumflex over ( )}(¬ybiI). As shown in the two panels below,the numbers at the x-axis correspond to the four corresponding barsabove the numbers. For example the first 4 bars correspond to the number1, the second set of 4 bars correspond to the number 2 and so forth.

FIG. 33 shows the response of the nemR promoter in E. coli MG1655 forspecifically sensing malathion. The nemR promoter responds to highconcentrations of malathion alone. As shown in the two panels below, thenumbers at the x-axis correspond to the four corresponding bars abovethe numbers. As shown in the two panels below, the numbers at the x-axiscorrespond to the four corresponding bars above the numbers. For examplethe first 4 bars correspond to the number 1, the second set of 4 barscorrespond to the number 2 and so forth.

FIG. 34 shows the response of the zraP promoter in E. coli MG1655 forspecifically sensing zinc. The zraP promoter responds specifically andmonotonically to increasing concentrations of zinc alone. As shown inthe two panels below, the numbers at the x-axis correspond to the fourcorresponding bars above the numbers. As shown in the two panels below,the numbers at the x-axis correspond to the four corresponding barsabove the numbers. For example the first 4 bars correspond to the number1, the second set of 4 bars correspond to the number 2 and so forth.

FIG. 35 shows the growth curves for the revival of engineered E. colistrains following freeze-drying with various cryoprotectants.Cryoprotectant experiments are annotated as follows: Cryoprotectant(Overnight Growth Medium/Revival Medium). Lyophilized (freeze-dried)cultures were stored for periods of 24 h, 1 wk, 2 wk, and 4 wk beforerevival in resuspension medium. All media contained the appropriateantibiotic for plasmid maintenance. S/T denotes a cryoprotectant mixtureof both sucrose and trehalose. LB and LB+glucose media exhibited thebest cryoprotective performance, as measured by lag time to exponentialgrowth phase. Traditional cryoprotectants exhibited slower revivaltimes.

FIG. 36 (FIG. 36A, 36B, 36C, 36D, 36E, 36F) shows on-chip time-lapseinduction responses of promoter constructs identified from theliterature, expressed in E. coli, and summarized in Table 2. 36A)MG1655/As3, MG1655/pRS18, and MG1655/As1 show high sensitivity to 0.13μM arsenic. (Note that pRS18, a plasmid generated in our lab forprevious work, shares the same promoter as Transcriptic plasmid As7.)36B) MG1655/Cd1 shows high sensitivity to 0.04 μM cadmium. 36C)LABEC01/Cr5 shows high sensitivity to 5 μM chromium(VI). 36D) MG1655/Cu1shows high sensitivity to 25 μM copper. 36E) LABEC01/Pb2 shows highsensitivity to 7 μM lead. 36F) MG1655/Hg3 shows high sensitivity to 0.1μM mercury.

FIG. 37 shows multiple expression systems for promoters identified usingRNA-Seq. For proper regulation of synthetic constructs, promoters willbe cloned into various copy number plasmids to maintain appropriateratios with low-copy native regulatory elements. In expression systemsusing Lux instead of GFP, the operon will be optionally split to removesubstrate limitation. The light-producing luxAB element will beoptionally integrated either downstream of a second copy of thecandidate promoter at a neutral site on the genome or directlydownstream of the gene regulated by the candidate promoter.

FIG. 38 shows side-by-side comparison of reporter systems on otherwiseidentical plasmid backbones demonstrates the superior detection limitsachieved by using luminescence.

FIG. 39 (FIGS. 39A, 39B, 39C, 39D, and 39E) shows an overview of oursensor prototype. 39A) We currently have five replicates of the sensorprototype running continuously in the lab. 39B) Sensor enclosure withfront panel opened to expose internal components. b1: Electronicssub-enclosure, b2: temperature controller, b3: AC power distributiondevices, b4: tri-output DC power supply, b5: Ziva optical assembly, b6:fan/heater, b7: DC power distribution block, b8: peristaltic pump. 39C)Microfluidic device being illuminated with the GFP excitation LED. 39D)Image taken using Ziva optics of eight strains growing inside ourmicrofluidic device and responding to induction. 39E) Representativetime series of data taken from one of the sensors. The green barrepresents the introduction of tracer dye, and the red bar representsthe introduction of the inducer, in this case mercury. Fluorescence ofthe mercury-specific strain rises and then falls as mercury isintroduced and removed.

FIG. 40 (FIGS. 40A, 40B, 40C, 40D, 40E and 40F) shows 8-strain chipinduction data for six toxins, demonstrating the sensor-specificinduction of the expected strains as well as any response fromnon-specific strains.

FIG. 41 (FIGS. 41A, 41B, 41C, 41D, 41E and 41F) shows 18-strain chipinduction data for three toxins, demonstrating the sensor-specificinduction of B. subtilis NCIB 3610/Amm3 for ammonium and the uniquecombinations of responses allowing the identification of cobalt andlead.

FIG. 42 shows 34 days of continuous fluorescence data from the E. coliMG1655/Cu1 strain as it responds to two inductions per day of varioustoxins and concentrations. Exposures to “on-target” toxins are shown inthe top (first) panel as the second bar, the fifth panel as the twelfthbar, the seventh panel as the second bar, the eighth panel as the fourthbar, and the ninth panel as the ninth and twelfth bar. Exposures to“off-target” toxins are the thick bars on all the panels that arehatched (excluding the described on-target toxins). Double-toxinexposures that include the “on-target” toxin are cross-hatched and areshown in the sixth panel as the fourteenth bar and in the seventh panelas the eighth bar. These green fluorescent pulses are shown as the thinbars with no hatching and appear as thin white bars.

FIG. 43 shows 34 days of continuous fluorescence data from the E. coliMG1655/Hg3 strain as it responds to two inductions per day of varioustoxins and concentrations. the exposures to “on-target” toxins are shownin the second panel as the second and sixth bar, the fourth panel as thesecond bar, the fifth panel as the eight, tenth and fourteenth bar, thesixth panel as the first and thirteenth bar, eighth panel as the sixthand eighth bar, the ninth panel as the seventh and eleventh bar, and thetenth panel as the second bar. Exposures to “off-target” toxins are thethick bars on all the panels that are hatched (excluding the describedon-target toxins). Double-toxin exposures that include the “on-target”toxin are cross-hatched and are shown in the seventh panel as the fourthand twelfth bar. These green fluorescent pulses are shown as the thinbars with no hatching and appear as thin white bars.

FIG. 44 shows inhibition of cellular esterase activity in S. cerevisiaeduring malathion exposure.

FIG. 45 shows the functional unit of the microfluidic “gill chip.”Medium is delivered via the main channel, and cells are cultured in thebranched channels.

FIG. 46 shows the optimization of cell trap length based on GFPexpression level. 120-mm-long traps provide the greatest fluorescentsignal for all arsenic concentrations.

FIG. 47 (FIG. 47A, 47B, 47C) shows the response of arsenic-sensingplasmid pRS18 (from previous work) to step-induction with arsenic.Images before (47A) and after (47B) induction with 0.1 μM arsenic. 47C)Measured GFP expression over time for induction with 0.1 μM arsenic.

FIG. 48 shows the response of copper-sensing plasmid pCueCopA (fromprevious work) to step-induction with copper. Measured GFP expressionover time for induction with 25 μM copper demonstrates that we candetect copper concentrations near the EPA limit

FIG. 49 shows the gill chip v9-C for parallelizing the on chip testingof toxin-responsive promoters. Four cell trapping regions (501) arevacuum loaded from four downstream fluidic ports. Long, serpentinechannels upstream of the trapping regions (502) serve as fluidic “bufferzones” to prevent the cross-contamination of strains. The three-port“Dial-A-Wave” module at the left serves to mix two medium streams toprecisely and dynamically define the toxin concentration in the celltrapping regions.

FIG. 50 (FIGS. 50A, 50B, 50C and 50D) shows batch growth rate data forE. coli MG1655 cells exposed to 50B) copper, 50C) mercury, and 50D)chromium(VI). Panel 50B) shows the approximately linear scaling of lagprior to exponential growth phase with copper concentration.

FIG. 51 shows the response of the arsR promoter in E. coli MG1655 forspecifically sensing arsenic. Note that the arsR promoter respondsspecifically and monotonically to increasing concentrations of arsenicalone and in combination with other heavy metals (rightmost conditions)without exhibiting crosstalk. Plots represent the mean fold change, thenormalized counts (taking into account both the library depth and thegene length), and the “% CDF expression level.” This measure representsthe position of the gene in the Cumulative Distribution Function (CDF)obtained from the normalized counts for a given experimental condition;that is, a large CDF indicates a high expression level for the genecompared to the other genes in the same experiment. Replicate samplesfor each toxin are shown in the same color. Control samples for eachbatch of experiments are shown in white. As shown in the two panelsbelow, the numbers at the x-axis correspond to the four correspondingbars above the numbers. For example the first 4 bars correspond to thenumber 1, the second set of 4 bars correspond to the number 2 and soforth.

FIG. 52 shows the validation of five cobalt-sensing promoters (¬Co3,Co4, Co6, Co7, Co8) identified via RNASeq analysis with E. coli and one(Co2) identified from literature.

FIG. 53 shows the CAD design for a highly parallel microfluidic devicecapable of culturing 512 unique toxin-sensing promoter strains inindividually addressable “gill” cell-trapping regions. Two inlet portsat the top are combined and mixed during passage through staggeredherringbone mixers (541) before branching into 512 “gill” trappingregions (542) and recombining at the outlet port. The footprint of theentire device is small enough to fit on a 1″×3″ glass slide.

FIG. 54 (FIGS. 54A, and 54B) shows images showing the on-chip revival oflyophilized sensor strain pLB-As7 (MG1655). Device image 54A) 0.5 hafter chip wetting and 54B) after 11 hours of robust post-revivalgrowth. The strain was loaded, grown to confluence, lyophilized inLB+0.4% sucrose+spectinomycin for 17 h (9 h freeze, 8 h dry), stored for24 h, and revived in LB+spectinomycin.

FIG. 55 (55A and 55B) shows a comparison of optical systems. The samemicrofluidic device was imaged with a) low cost biosensor opticsdeveloped by the Ziva Corporation and b) a research grade Olympus IX81microscope. While the Ziva image may appear inferior, this is anartifact due to zooming of the image for comparison (the Ziva system isdesigned with a wide field of view compared to the Olympus). We haveverified that the Ziva system yields images that equal the quality ofthe Olympus system, which is remarkable given the difference in cost($2K for Ziva versus $125K for Olympus).

FIG. 56 shows a CAD drawing of the 18-strain microfluidic chip used tocollect toxin response data from 18 different toxin-specific strainssimultaneously. Left) View of the entire microfluidic device. Theresistance to each cell trapping region is identical, ensuringequivalent flow to all areas of the device. Black channels: E. colianalyte distribution channels. channels 1: B. subtilis analytedistribution channels (573). rectangle (571): magnified area shown inthe right panel. Right) The field of view captured by our imagingsystem. Red channels—(571): B. subtilis “gill” trapping regions.channels (572): E. coli “gill” trapping regions. The collections ofrectangles flanking the lower E. coli trapping region are present toassist in automatic image registration.

FIG. 57 shows on left panel: the template image used to align the imagesfrom all of the devices. The Right panel: the result of applying thealgorithm to an image.

FIG. 58 shows an example of the results of the image transformationafter applying the alignment, compression, and feature extraction on theGFP signal. This is the input signal that is provided to the classifier.The left square is the original image, and the other three squares showthe changes made by the image processing techniques.

FIG. 59 (59A-59D) shows parallelized microfluidic device housing 2,048unique engineered strains from two microbes in panels A, B, C and D. Asshown a single strain bank within this device is shown in FIG. 1A. Eachstrain is spotted within a reservoir (1), where it expands via growththrough feeder channels (2) into ten vertical trapping chambers (3). Thecells proliferate in exponential phase within the trapping chambers dueto the delivery of fresh medium by convection through an adjacentchannel (4) and diffusion into the trapping chambers. Excessproliferating cells extend out of the trapping chamber into the mediumdelivery channel, where they are cleared by convective flow. Trappingchambers remain densely packed due to cross-seeding by neighboring gillsvia a linker channel (5). The exponentially-growing culture in thetrapping regions is imaged periodically to measure the reporter responseto various agents introduced through the medium delivery channel.

FIG. 60 shows a microfluidic strain bank format compatible with a6,144-well SBS microplate.

DEFINITIONS

The term “response element” refers to sequences of DNA that are able tobind specific transcription factors or analytes and regulatetranscription of genes. Specific response elements are described hereinand in Intl. Appl. No. PCT/US2012/069914, hereby incorporated herein inits entirety for all purposes.

The term “analyte” refers to any compound o5 agent of interest fordetection. As appropriate, the analyte can be an element, a nucleicacid, a protein, a carbohydrate, a lipid or a small organic compound.The analyte can be organic or inorganic.

The terms “identical” or percent “identity,” and variants thereof in thecontext of two or more polynucleotide sequences, refers to two or moresequences or subsequences that are the same. Sequences are“substantially identical” if they have a specified percentage of nucleicacid residues or nucleotides that are the same (e.g., at least 60%identity, optionally at least 65%, 70%, 75%, 80%, 85%, 91%, 92%, 93%,94%, 95%, 96%, 97%, 98%, or 99% identity over a specified region (or thewhole reference sequence when not specified)), when compared to areference sequence (e.g., SEQ ID NOs: 1-43) and aligned for maximumcorrespondence over a comparison window, or designated region asmeasured using one of the following sequence comparison algorithms or bymanual alignment and visual inspection. The present invention includespolynucleotides improved for expression in host cells that aresubstantially identical to the polynucleotides described herein.Optionally, the identity exists over a region that is at least about 50nucleic acid bases or residues in length, or more preferably over aregion that is 100, 200, 300, 400, 500, 600, 800, 1000, 1500, 2000,2500, 3000, or more, nucleic acids in length, or over the full-length ofthe sequence. In some alternatives described herein, the identity existsover a region that is at least about 50 nucleic acid bases or residuesin length, or more preferably over a region that is 100, 200, 300, 400,500, 600, 800, 1000, 1500, 2000, 2500, 3000, or more, nucleic acid basesin length, or over the full-length of the sequence, or any number ofbases defined by a range in between any two aforementioned values.

For sequence comparison, typically one sequence acts as a referencesequence, to which test sequences are compared. When using a sequencecomparison algorithm, test and reference sequences are entered into acomputer, subsequence coordinates are designated, if necessary, andsequence algorithm program parameters are designated. Default programparameters can be used, or alternative parameters can be designated. Thesequence comparison algorithm then calculates the percent sequenceidentities for the test sequences relative to the reference sequence,based on the program parameters. Without being limiting, the percentsequence identities for the test sequences relative to the referencesequence can be calculated by a program such as BLAST using the defaultparameters.

The term “comparison window”, and variants thereof, includes referenceto a segment of any one of the number of contiguous positions selectedfrom the group consisting of from 20 to 600, usually about 50 to about200, more usually about 100 to about 150 in which a sequence may becompared to a reference sequence of the same number of contiguouspositions after the two sequences are optimally aligned. Methods ofalignment of sequences for comparison are well known in the art. Optimalalignment of sequences for comparison can also be conducted by the localhomology algorithm of Smith and Waterman Add. APL. Math. 2:482 (1981),by the homology alignment algorithm of Needle man and Wunsch J. Mol.Biol. 48:443 (1970), by the search for similarity method of Pearson andLipman Proc. Natl. Acad. Sci. (U.S.A.) 85: 2444 (1988), by computerizedimplementations of these algorithms (GAP, BESTFIT, BLAST, FASTA, andTFASTA in the Wisconsin Genetics Software Package, Genetics ComputerGroup (GCG), 575 Science Dr., Madison, Wis.), Karlin and Altschul Proc.Natl. Acad. Sci. (U.S.A.) 87:2264-2268(1990), or by manual alignment andvisual inspection (see, e.g., Ausubel et al., Current Protocols inMolecular Biology (1995 supplement)). Examples of an algorithm that issuitable for determining percent sequence identity and sequencesimilarity include the BLAST suite using default parameters, availableon the internet at blast.ncbi.nlm.nih.gov/, and known to those of skillin the art. In some alternatives, a “comparison window” is made, andcomprises variants thereof, and can include reference to a segment ofany one of the number of contiguous positions selected from the groupconsisting of from 20 to 600, usually about 50 to about 200, moreusually about 100 to about 150 in which a sequence may be compared to areference sequence of the same number of contiguous positions after thetwo sequences are optimally aligned or any number defined by a rangewithin any to aforementioned values.

The term “substantial identity” of polynucleotide sequences means that apolynucleotide comprises a sequence that has at least 80% sequenceidentity, e.g., at least about 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, 99%, or higher, compared to a reference sequence (e.g., SEQ IDNOs: 1-45), using sequence alignment/comparison algorithms set tostandard parameters. One of skill will recognize that these values canbe appropriately adjusted to determine corresponding identity ofproteins encoded by two nucleotide sequences by taking into accountcodon degeneracy, amino acid similarity, reading frame positioning andthe like.

“Substantial identity” of amino acid sequences for these purposes meanssequence identity of at least 80% sequence identity, e.g., at leastabout 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or higher,using sequence alignment/comparison algorithms set to standardparameters. Polypeptides which are “substantially similar” sharesequences as noted above except that residue positions which are notidentical may differ by conservative amino acid changes. Conservativeamino acid substitutions refer to the interchangeability of residueshaving similar side chains. For example, a group of amino acids havingaliphatic side chains is glycine, alanine, valine, leucine, andisoleucine; a group of amino acids having aliphatic-hydroxyl side chainsis serine and threonine; a group of amino acids having amide-containingside chains is asparagine and glutamine; a group of amino acids havingaromatic side chains is phenylalanine, tyrosine, and tryptophan; a groupof amino acids having basic side chains is lysine, arginine, andhistidine; and a group of amino acids having sulfur-containing sidechains is cysteine and methionine. Preferred conservative amino acidssubstitution groups are: valine-leucine-isoleucine,phenylalanine-tyrosine, lysine-arginine, alanine-valine, asp articacid-glutamic acid, and asparagine-glutamine. Determination of“substantial identity” can be focused over defined subsequences, such asknown structural domains.

Another indication that nucleotide sequences are substantially identicalis if two molecules hybridize to each other, or a third nucleic acid,under stringent conditions. Stringent conditions are sequence dependentand will be different in different circumstances. Generally, stringentconditions are selected to be about 5° C. lower than the thermal meltingpoint (Tm) for the specific sequence at a defined ionic strength and pH.The Tm is the temperature (under defined ionic strength and pH) at which50% of the target sequence hybridizes to a perfectly matched probe.Typically, stringent conditions will be those in which the saltconcentration is about 1 molar at pH 7 and the temperature is at leastabout 60° C.

An “expression cassette” refers to a nucleic acid construct, which whenintroduced into a host cell, results in transcription and/or translationof a RNA or polypeptide, respectively.

The term “promoter” or “regulatory element” refers to a region orsequence determinants located upstream or downstream from the start oftranscription that direct transcription. As used herein, a promoterincludes necessary nucleic acid sequences near the start site oftranscription, such as, in the case of a polymerase II type promoter, aTATA element. A promoter also optionally includes distal elements, whichcan be located as much as several thousand base pairs from the startsite of transcription. A “constitutive” promoter is a promoter that isactive under most environmental and developmental conditions. An“inducible” promoter is a promoter that is active under environmental ordevelopmental regulation. The term “operably linked” refers to afunctional linkage between a nucleic acid expression control sequence(such as a promoter) and a second nucleic acid sequence, such as anucleic acid encoding an antigen, wherein the expression controlsequence directs transcription of the nucleic acid corresponding to thesecond sequence. The promoters used in the present expression cassettesare active in the host cells, but need not originate from that organism.It is understood that limited modifications can be made withoutdestroying the biological function of a regulatory element and that suchlimited modifications can result in regulatory elements that havesubstantially equivalent or enhanced function as compared to a wild typeregulatory element. These modifications can be deliberate, as throughsite-directed mutagenesis, or can be accidental such as through mutationin hosts harboring the regulatory element. All such modified nucleotidesequences are included in the definition of a regulatory element as longas the ability to confer expression in the host cell is substantiallyretained. Without being limiting, some examples of promoters are listedin Table 1. As shown in Table 2 are more examples of toxin responsivepromoter constructs identified from the literature, synthesized byTranscriptic, cloned into E. coli, and demonstrating high sensitivity inthe microfluidic device, ordered by toxin. The promoter source and RBSused in the synthetic construct are shown alongside the toxinconcentration sensed and SNR after 6 h. (Refer to FIG. 36 for on-chiptime-lapse induction responses).

A detectable agent can be a nucleic acid, detectable protein,antibody-linked reporter protein, enzymatic assay product, orelectrochemical reaction product. A detectable agent can also be areporter protein that can be detected by an antibody. A detectable agentcan be a nucleic acid or a protein that can be assayed to determine aconcentration or a signal in response to a detectable analyte such as atoxin. The nucleic acid can be an RNA or a DNA that is transcribedfollowing a promoter being modulated by a signal.

A “reporter protein” as described herein, refers to a protein that isdetected which is indicative of transcription or translation from aregulatory sequence of interest in a bacteria, cell culture or animal. Areporter gene is a gene that is attached to a regulatory sequence ofanother gene. These can be used to indicate whether a certain gene isexpressed in the presence of an analyte. Without being limiting commonreporter genes to express a reporter proteins can be green fluorescentprotein, luciferase (which can catalyze a reaction with luciferin toproduce light, and red fluorescent protein. Without being limiting acommon reporter in bacteria is E. coli lacZ gene, which encodesbeta-galactosidase which can cause bacteria to appear in a blue colorwhen grown in a medium that contains the substrate X-gal.

In regards to an “electrochemical reaction product” detection method, insome embodiments there has been success in detecting hydrogen peroxide(H₂O₂) produced by reactive oxygen species formed when green fluorescentprotein molecules are illuminated within their excitation spectrum. ThisH₂O₂ is detected at microelectrodes integrated into the microfluidicdevice. The microelectrodes may be functionalized by coating them with athin film (for example, Prussian blue) to increase sensitivity andselectivity. They may also be coated with a protectant (for example,Nafion) to prevent fouling. Microelectrodes may be positioned in thesame fluidic channel as the cells or in an adjacent fluidic channel,separated by a thin barrier of PDMS. The latter sensing methodology maylimit chemical fouling of the microelectrode surface over longmeasurement durations and is feasible due to the ability of H₂O₂ todiffuse through PDMS. In some embodiments, an electrochemical reactionproduct is product that can produce a detectable electric current. Thesetypes of reactions can involve electric charges that can move betweenthe electrode and the electrolyte. In some embodiments, of themicrofluidic devices described herein, the microfluidic devices comprisemicroelectrodes integrated into the microfluidic device. In someembodiments, the microelectrodes may be functionalized by coating themwith a thin film (e.g. Prussian blue) to increase sensitivity andselectivity. In some embodiments, the microelectrodes are coated with aprotectant (e.g. Nafion) to prevent fouling. In some embodiments, themicroelectrodes are positioned in the same fluidic channel as the cellsor in an adjacent fluidic channel, separated by a thin barrier of PDMS.

“Enzymatic assay product” as described herein, can be a product or aprotein that is usually detected from an enzymatic reaction. Withoutbeing limiting, one example would be to engineer the cells to producethe beta-galactosidase enzyme (e.g. lacZ for bacteria). The medium canthen be supplemented with the organic compound X-gal (BCIG, for5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside), and thebeta-galactosidase enzyme would hydrolyze this to an insoluble bluecompound that is detectable by an imaging system. Alternatively, anotherway to assay for the enzymatic assay product is to engineer the cells toproduce the beta-galactosidase enzyme. The medium would then besupplemented with LuGal, a soluble conjugate of luciferin and galactose,and the beta-galactosidase enzyme would hydrolyze this to luciferin.Effluent from each strain would be collected from the microfluidicdevice and subjected to a luciferase assay for the sensitive detectionof luciferin. In some embodiments, a microfluidic device is provided,wherein the microfluidic device comprises one or more colonies orcultures of microorganism cells at one or more predetermined addressablelocations, wherein each of the cells within the one or more colonies orcultures comprises an expression cassette comprising a biosensor orpromoter operably linked to a polynucleotide encoding a detectableagent, wherein transcription of the biosensor or promoter is modulatedby the presence of an analyte. In some embodiments, the detectable agentis a nucleic acid, detectable protein, antibody-linked reporter protein,enzymatic assay product, or electrochemical reaction product. In someembodiments, the detectable agent is an enzymatic assay product. In someembodiments, the enzymatic assay product is beta-galactosidase enzyme.In some embodiments, the detectable agent is detected by addition ofX-gal or LuGal.

TABLE 1 Identified promoters from literature Toxin Source OrganismGene/Promoter Arsenic E. coli plasmid arsR/p_(arsR) E. coli genomearsR/p_(arsR) S. aureus plasmid arsR/p_(arsR) B. subtilis genomearsR/p_(arsR) Cadmium S. aureus plasmid cadC/p_(cadC) P. putida genomecadR/p_(cadR) S. salivarius genome cadX/p_(cadX) S. lugdunensis genomecadX/p_(cadX) Chromium(VI) C. metallidurans plasmid chrB/p_(chrB) O.tritici transposon chrB/p_(chrB) B. subtilis genome chrS/p_(chrS) CopperE. coli genome cueR/p_(copA) E. coli genome (cusS/R)/p_(cusC) Lead C.metallidurans plasmid pbrR/p_(pbrR) Mercury E. coli plasmidmerR/p_(merR) S. aureus plasmid merR/p_(merR) S. marcescens plasmidmerR/p_(merR) S. lividans genome merR/p_(merR) Ammonia B. subtilisgenome P_(nasA) B. subtilis genome P_(nasB) B. subtilis genomeP_(spo1-tnrA)

TABLE 2 Concentration Refer Sensed in SNR to Microfluidic after FigureToxin Gene/Promoter Source RBS Host Strain/Plasmid Device (μM) 6 h PanelArsenic arsR/p_(arsR) E. coli plasmid native E. coli MG1655/As1 0.13 2036A Arsenic arsR/p_(arsR) E. coli genome synthetic E. coli MG1655/As30.13 33 36A Cadmium cadC/p_(cadc) S. aureus plasmid native E. coliMG1655/Cd1 0.04 17 36B Chromium(VI) chrB/p_(chrB) O. tritici transposonnative E. coli LABEC01/Cr5 5 5 36C Copper cueR/p_(copA) E. coli genomenative E. coli MG1655/Cu1 25 65 36D Lead pbrR/p_(pbrR) C. metalliduransplasmid synthetic E. coli LABEC01/Pb2 7 18 36E Mercury merR/p_(merR) E.coli plasmid synthetic E. coli MG1655/Hg3 0.1 20 36F

DETAILED DESCRIPTION Introduction

Disclosed are methods, materials and devices that pertain to a robustmicrofluidic biosensor that is suitable for continuously monitoringtoxin levels in sources such as water supplies, and runs freely for 30days without intervention. In some embodiments, the device and/ormicrofluidic biosensor can run for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29or 30 days or any number of days defined by a range between any twoaforementioned values, without intervention. The invention isinexpensive, able to detect more toxins than conventional means, and canbe deployed with minimal or no infrastructure. A functional workingembodiment is described. Applications include general water supplymonitoring and defense against terrorist water supply attacks.

Also provided are methodologies to pre-load and freeze-dry an array ofbacterial sensor strains within a microfluidic chip to increase survivalrates during long-term storage. Upon device deployment, strains arerevived on-chip using a mixture of sampled water and concentrated growthmedium for time-lapse fluorescence imaging. An on-board computeranalyzes the acquired images in real time before generating andwirelessly transmitting a toxin signature to a secure database. In someembodiments, the device successfully detects the presence of heavymetals and ammonia at levels relevant to drinking water safety and canbe easily adapted to sense other chemicals of interest.

In some embodiments, methods are provided for preloading and freezedrying bacterial sensor strains within a microfluidic chip to increasetheir survival rates for long term storage. In some embodiments, thechips can be stored for 1 month, 2 months, 3 months, 6 months, 12months, or any amount of time defined by a range set forth in any of theaforementioned values. In some embodiments, the bacterial sensor strainsare preloaded as a liquid culture. In some embodiments, the liquidculture comprises 0.1%, 0.2%, 0.3%, 0.4% 0.5%, 1%, 5%, 10%, 20%, 30%,40%, 50%, 60%, 70% or 80% glycerol or any percent amount of glycerolwithin a range in between any aforementioned values. Alternatively, anyviscous solution for the storage of bacteria at low temperature can beused in order to cryopreserve the bacteria. Such solvents are known tothose skilled in the art.

In varying embodiments, the devices can comprise an enclosure whichhouses a microscope, computer, heating element, peristaltic pump, andmicrofluidic chip. The microfluidic chip can be pre-loaded with multipledistinct strains of bacteria, each of which has been geneticallyengineered to produce a detectable agent such as fluorescent protein(FP) (e.g., GFP) or derivative thereof in response to toxin. In someembodiments, the bacteria can be loaded within a liquid culture, inwhich the bacteria has an OD₆₀₀ (i.e. concentration) of between 1 and 2.In some embodiments, the bacteria are loaded into the chip as a smallvolume of culture, in which the culture contains the bacteria to beloaded. In some embodiments, the culture comprises glycerol. Glycerolcan be used as a cryoprotectant for the cells. Without being limiting,examples of fluorescent proteins can include cyan fluorescent protein,green fluorescent protein, yellow fluorescent protein, red fluorescentprotein, and far-red fluorescent protein. In some alternatives describedherein, the fluorescent protein can be cyan fluorescent protein, greenfluorescent protein, yellow fluorescent protein, red fluorescentprotein, and far-red fluorescent protein.

The pump can draw water from the water supply and feed it into themicrofluidic chip, where it is mixed with a concentrated M9 minimalbacterial growth medium and flows past the bacterial traps. Thefluorescence microscope is used to image the bacteria at 5-minutesintervals to determine the FP expression level of each strain. In someembodiments, the bacteria are imaged at 1, 2, 3, 4, 5, 6 7, 8, 9, or 10minute intervals, or any amount of time between a range between any twoaforementioned values. This can give a real-time readout of toxin levelsas they enter the water supply. Each strain has been engineered with aDNA construct comprising the operably linked elements of an antibioticresistance gene, a promoter that either increases or decreasestranscription levels specifically in response to one or more toxins, anda gene encoding an FP. For Escherichia coli strains, these constructscan be integrated on a plasmid, e.g., with the p15A origin ofreplication. For Bacillus subtilis strains these constructs can beintegrated into the genome.

There are several aspects to the development of the biosensor such asthe application of synthetic biology to develop novel microbial sensorstrains that will have sensitive and specific responses to analytes suchas critical water toxins, and the use of state-of-the-art microfluidictechniques and optical technology along with computational biology todetect and interpret the signals from these analyte-sensing organisms.

Some embodiments focus on biological aspects, with the goal ofidentifying combinations of cellular signals that can be harnessed toprovide specific responses to the presence of a range of analytes suchas potential water toxins. The literature was searched to identify knowncellular signaling pathways responsive to toxins of interest andselected several candidate promoters from a variety of microbialorganisms. In an exemplary embodiment, the plasmids were designed witheach of these promoters driving GFP and these sequences wereconstructed. As proof of principle, microfluidics were used to test twosuch plasmids that were built in-house. These preliminary sensor strainswere subjected to various toxin levels within a novel microfluidic chip,and bright response signals were observed in some alternatives describedherein. In addition to taking advantage of known toxin-sensitivepathways, a program of Next Generation Sequencing was conducted togreatly expand the number of known response promoters for each toxin.Novel RNA-Seq analysis algorithms were also developed to identifyspecific differentially expressed genes in our large data set. Thepromoter regions were located for the most promising differentiallyexpressed genes and sensor circuits based on them were designed. Theconstruction of plasmid-based microbial sensor strains was alsocompleted for all toxins, based on promoters identified via literaturesearches and RNA-Seq. In some embodiments, microfluidics were also usedto demonstrate the proper induction of each strain by various levels ofthe relevant toxin.

Another exemplary embodiment focused primarily on mechanical sensordevelopment, including the microfluidic device design, opticaltechnology, and computational tools required to translate a series ofoptical signals from multiple sensor strains into a meaningful toxinlevel determination. In an exemplary embodiment, a microfluidic devicewas also developed to culture and sequentially expose an array ofsensing strains to various levels of toxins of interest over a period ofseveral weeks. This “gill” chip contains tall cell traps that provide abright fluorescent signal from a large population of cells. In someembodiments described herein, complimentary technologies were alsodeveloped that allowed one to mix concentrated media with a naturalwater source and to dispose of the microbial species safely upon exitingthe device. A partnership with the Ziva Corporation was also developedto make a low cost, field capable optical system to image ourmicrofluidic devices. The capabilities of this optical systemdemonstrated that it was comparable to a research grade microscope atlow optical power. To enable viable long-term storage of biosensorchips, an embodiment is described in which a method was developed todeposit and freeze-dry strains in a defined array within ourmicrofluidic device. The successful revival of our strains weredemonstrated after four weeks of room temperature storage with littleloss of viability. In some embodiments, herein, the strains can berevived at room temperature for use in a test for metals. Lastly,replacing the GFP reporter with bacterial luciferase toward the goal ofincreasing SNR by eliminating background autofluorescence wasinvestigated. In an exemplary embodiment, an arsenic sensor modified inthis manner shows much greater sensitivity, even compared to analyticalmethods approved by the EPA for detecting arsenic in drinking water.Strain response data was also used to train the classifier to identifythe presence of each toxin of interest in a water source of unknowncomposition in real time. Furthermore, classifier performance wasstrengthened by acquiring long data sets (up to 50 days) of both on- andoff-target toxin exposures within the microfluidic devices. In someembodiments, the data sets are acquired for 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 20, 30, 40 or 50 days or any number of days between a ranges definedbetween any two aforementioned values.

In another exemplary embodiment, technologies were developed to make aninexpensive and robust sensor prototype that can be deployed in realbodies of water to continuously monitor for toxin contamination. In someembodiments, a peristaltic pumping system is developed to mix water andgrowth media on-chip and a filter utilized to prevent clogs in themicrofluidic device. In another embodiment, a comprehensive softwarepackage is developed to control fluid flow, image acquisition, andwireless data transfer. A solar energy harvesting system was alsoconstructed for powering the device in an off-grid field environment.The development of a functional prototype of our biosensor for use incomprehensively testing our sensor strains was also completed. Thebiological, fluidic, and computational components were integrated into atemperature-controlled enclosure containing all optics and electronicsnecessary to draw a water sample, acquire images of the biosensorstrains within the microfluidic device, analyze this data using theembedded classifier algorithm, and transmit the results via encryptedWi-Fi.

In an exemplary embodiment, the final deliverable is a self-containedwater sensor prototype. Below, are several alternatives described hereinwhich have results that demonstrate the ability to acquire and analyzedata in real time to provide an accurate and continuous determination ofwater quality.

These results mark some conclusions of an exemplary embodiment thatcombined Next Generation Sequencing, genetic engineering, andmicrofluidic technology to precisely engineer a highly sensitive andspecific biosensor platform that can continuously monitor water suppliesfor the presence of chemical toxins. In the following alternatives, theupdate section describes several milestones along with related data andresults demonstrating their achievement.

An overview of the sensor can be seen in FIG. 39. In order to acquirethe large amount of data required to successfully train the classifier,five replicates of the fully self-contained device prototype were built,in which the microfluidic devices are mounted and imaged usingcustom-designed optics as shown in some embodiments herein (Milestone6.9). The majority of the data for this project was acquired using anovel microfluidic device capable of housing eight different bacterialstrains that receive media from a shared source. This enables thesimultaneous testing of the responses of eight different strains to eachtoxin concentration, providing information on specific and non-specificresponses, which can be used to strengthen the classifier (FIG. 39D).The design was expanded to house 18 strains and we have begun takingdata with this new chip to provide larger datasets to the classifier. Adetailed CAD drawing of this device can be seen in FIG. 56. The 18 celltrapping regions are vacuum-loaded from 18 downstream fluidic ports.Long, serpentine channels upstream of the trapping regions serve asfluidic buffer zones to prevent the cross-contamination of strains whileloading. The toxin concentration is controlled dynamically by aperistaltic pump that mixes a water/toxin combination (representing thesource water stream) with a concentrated medium stock.

Importantly, the ability to house many strains on the same chip andsubject them to the same toxin inductions enables one to look not onlyat toxin-specific induction but at crosstalk as well. In FIG. 40, asingle induction time series plot for each of the following six toxinsis shown: arsenic, cadmium, chromium(VI), copper, lead, and mercury.Each induction plot shows the fluorescence induction ratio (calculatedas (mean fluorescence following induction)/(mean fluorescence prior toinduction)) for multiple strains being cultured in the chip, where thetoxin is introduced at t=0 and replaced by pure water at t=2 hours. Ineach panel, the time series data for the “on-target” responding strainis plotted in bold. As expected, the GFP fluorescence traces for the “ontarget” responding strains increase with induction and decrease withun-induction, with some delay due to the cellular response machinery.FIG. 40A shows strain E. coli MG1655/As7 sensitively responding to 0.2μM arsenic, with E. coli MG1655/Pb7 exhibiting a smaller response. Itwas discovered in several experiments that strain E. coli MG1655/Pb7 isa less specific responder that aids in toxin identification mainlythrough combinatorics. FIG. 40B shows strain E. coli MG1655/Cd1 and E.coli MG1655/Hg3 responding to 0.05 μM cadmium, with E. coli MG1655/Pb7exhibiting a much larger response. FIG. 40C shows strain E. coliMG1655/Cr11 and E. coli MG1655/Co7 responding to 0.5 μM chromium(VI),with E. coli MG1655/Pb7 exhibiting an equal magnitude but more rapidresponse. FIG. 40D shows strain E. coli MG1655/Cu1 sensitivelyresponding to 5 μM copper, with E. coli MG1655/Pb7 exhibiting a smallerresponse. FIG. 40E shows strain E. coli MG1655/Pb7 responding to 1.8 μMlead, with E. coli MG1655/Cd1 and E. coli MG1655/Cu1 responding atlesser magnitudes. Finally, FIG. 40F shows strain E. coli MG1655/Hg3sensitively responding to 0.2 μM mercury.

In an embodiment herein, the toxin response data across 18 uniquesensing strains were also collected using a new chip design that houses16 E. coli strains and 2 B. subtilis strains. This chip was initiallyused to test the ammonium sensor, with a representative induction shownin FIGS. 41A and 41B. The B. subtilis NCIB 3610/Amm3 construct is a“lights-off” sensor, meaning that GFP production drops when ammonium isintroduced, following a slight increase due to faster growth on thispreferred nitrogen source. This behavior was observed in response to 5ppm NH4-N in FIG. 41C. The two B. subtilis “gill” regions harboring theAmm3 strain respond sensitively to ammonium, whereas various strains inthe E. coli “gill” regions shown in FIG. 41A do not. Additional toxinexposures of previously untested strains in this 18-strain chipdemonstrated the ability to sense cobalt, an additional toxin notincluded in our primary list of eight. It was discovered that strain E.coli MG1655/Pb7 responds sensitively to cobalt, whereas no other E. colior B. subtilis strain does (see FIG. 41C, 41D). It was found thatstrains E. coli MG1655/Zn6, E. coli MG1655/Pb7, and E. coli MG1655/Pb8respond sensitively to lead in descending magnitude, whereas no otherstrains do (see FIG. 41E, 41F). By combining these sensor responses, wecan identify cobalt by the unique response of Pb7 alone and lead by theunique combination of Zn6, Pb7, and Pb8 responses.

To date, 247 on-chip toxin exposures with these five prototype sensorswere performed, capturing a total of 109,000 transmitted light andfluorescence images. A single microfluidic chip was run for up to 50days, which includes inducing and un-inducing the strains with varioustoxins twice each day. FIG. 42 shows the response of a singlecopper-sensitive strain periodically exposed to toxins and imaged over aperiod of 34 days, where each row contains approximately three days ofdata. Mean fluorescence of cells within the trapping region is plottedover time, and toxin exposure events are color coded. Exposures to“on-target” toxins are shown in the top (first) panel as the second bar,the fifth panel as the twelfth bar, the seventh panel as the second bar,the eighth panel as the fourth bar, and the ninth panel as the ninth andtwelfth bar. Exposures to “off-target” toxins are the thick bars on allthe panels that are hatched (excluding the described on-target toxins).Double-toxin exposures that include the “on-target” toxin arecross-hatched and are shown in the sixth panel as the fourteenth bar andin the seventh panel as the eighth bar. Toxin-spiked water samples weredrawn through peristaltic pump tubing to the cells with delays rangingfrom 1-9 hours, depending on the pump speed and length of tubing. Insome embodiments, the delays can be for 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10hours or any amount of time in between a range defined by any twoaforementioned values. Current long delays are due solely to slow pumprates and long tubing lengths in our initial prototype and not to anyfundamental characteristic of the genetic circuits or microfluidicdevices. As such, in some embodiments, they are minimized in designiterations. To visualize and accurately determine the moment of cellularexposure to toxin, a 20-minute pulse of green fluorescent tracer dye wasprovided, followed by a 20-minute pulse of pure water before flowing inthe toxin. These green fluorescent pulses are shown as the thin barswith no hatching and appear as white bars.

In FIG. 42, all the “on-target” copper exposures are followed by a risein mean cellular fluorescence. This indicates that the E. coliMG1655/Cu1 strain responds sensitively to copper. Conversely, themajority of “off-target” toxin exposures are not followed by a rise inmean fluorescence. This indicates that the E. coli MG1655/Cu1 strainresponds quite specifically to copper. For comparison, FIG. 43 shows theresponse of the E. coli MG1655/Hg3 strain to various toxin exposuresover the same 34-day period. The initial “on-target” mercury exposuresthrough 2/16 were intended to establish our sensing ability. Thedetection limit of this strain was subsequently probed by flowing in lowconcentrations of mercury, which did not elicit a response. Strain E.coli MG1655/Hg3 also exhibits less specificity than strain E. coliMG1655/Cu1, as indicated by rises in mean fluorescence following“off-target” inductions. This has not been problematic, as a uniquecombination of strain responses that is specific for mercury wereidentified. Movies of the 8-strain single-chip experiment analyzed inFIGS. 42 and 43 can be found at http: //biodynamics.ucsd.edu/DARPAmovies/index DARPA.html. As shown in FIG. 43, the exposures to“on-target” toxins are shown in the second panel as the second and sixthbar, the fourth panel as the second bar, the fifth panel as the eight,tenth and fourteenth bar, the sixth panel as the first and thirteenthbar, eighth panel as the sixth and eighth bar, the ninth panel as theseventh and eleventh bar, and the tenth panel as the second bar.Exposures to “off-target” toxins are the thick bars on all the panelsthat are hatched (excluding the described on-target toxins).Double-toxin exposures that include the “on-target” toxin arecross-hatched and are shown in the seventh panel as the fourth andtwelfth bar. These green fluorescent pulses are shown as the thin barswith no hatching and appear as white bars.

Transitioning at least one candidate toxin-sensitive promoter for eachtoxin from RNA-Seq analysis to microscope experiments to prototypesensor experiments with the exception of our initial construct based onthe single promoter candidate for malathion (Mall), which did notproduce a discernable response upon loading into the sensor device, wassuccessful. Additional constructs based on this promoter were generatedwhile collecting on-chip response data for all other sensing strainsusing the sensor prototypes was performed. After some investigation, amore robust malathion detection scheme was discovered that leverages itsesterase inhibition effect in yeast. S. cerevisiae was loaded into agill chip and CFDA (carboxyfluorescein diacetate) was added to thegrowth medium. Because CFDA permeates the cell membrane and is cleavedby cellular esterases into a fluorescent product, it was expected thatthe inhibition of esterase activity by malathion would decrease cellfluorescence. This is what was observed during two sequential exposuresto malathion in FIG. 44. GFP signal significantly drops in the presenceof 0.725 g/l malathion, both at around 1,000 and 3,000 min, andsubsequently recovers. Because CFDA breaks down in the medium over timethereby increasing the background signal, fresh medium+CFDA wasintroduced at 1,500 and 2,000 min. This explains the drift in baselineGFP during this period, which can be eliminated in subsequentexperiments by mixing dye and medium on-chip.

The successful measurement of esterase inhibition by malathion inwildtype yeast illustrates the power of our forthcoming whole-genomedetection scheme, which will be continually measured for the responsesof 2,000 to 4,000 E. coli and S. cerevisiae promoters arrayed in amicrofluidic chip. It is expected that the observed esterase inhibitionwill to be significantly represented in the genomic response of thismulti-strain library during malathion exposure.

Additional Alternatives

The following alternatives are offered to illustrate, but not to limitthe claimed invention.

Alternative 1 Microfluidic Aqueous Biosensor Device Methods

Clone the promoters into an expression plasmid driving the production ofGFP: Synthetic constructs were transformed into both standard E. coliMG1655 (ATCC 700926) and optimized E. coli LABEC01 cells for expressionanalysis. By serially passing MG1655 in M9 medium through severalgenerations, we evolved the common MG1655 lab strain, which has beenadapted for growth in rich lysogeny broth (LB) medium, into a strainwell-suited to growth in M9 minimal medium within our microfluidicdevices. The M9-adapted strain, which we named LABEC01, exhibitedmultiple phenotypic changes. The growth rate in minimal medium increasedby approximately 10%, colonies on minimal medium plates were observed tobe smoother as compared to the parent MG1655, and the bacteriaaggregated less when grown in microfluidic devices. To investigate howthese adaptations affect the cellular response to toxins and theactivity of sensitive promoters, we also exposed strain LABEC01 to thefull set of toxins for RNA-Seq analysis. Table 3 shows a list ofpromoters. In some embodiments, the promoters used for the detection ofthe toxin are provided in Table 3 and can be turned on by ammonia,arsenic, cadmium, chromium (VI), cobalt, copper, lead, malathion,mercury, and zinc.

In some embodiments a method of making an expression vector for thedetection of a toxin is provided. The method can comprise insertion of apromoter into an expression vector, wherein the promoter is operablylinked to a sequence encoding a reporter protein. Transcription from thepromoter can be increased or decreased by the presence of an analyte,such as a toxin. In some embodiments the toxin is ammonia, arsenic,cadmium, chromium (VI), cobalt, copper, lead, malathion, mercury orzinc.

Table 3 lists all candidate toxin-responsive promoters identified inthis work, ordered by the toxin of expected sensitivity. In the case ofpromoters identified by RNA-Seq, the gene is unknown. For promoters thathave been expressed in a synthetic construct, the selected RBS and hoststrain are shown. This synthetic construct has been used to sense thetoxin within a microfluidic device, the concentration sensed and SNRafter 6 hours are shown.

TABLE 3 Concentration Sensed in SNR Microfluidic after ToxinGene/Promoter Source RBS Host Strain/Plasmid Device (μM) 6 h Ammoniap_(nasA) B. subtilis genome native Ammonia p_(nasA) B. subtilis genomesynthetic Ammonia p_(nasB) B. subtilis genome native Ammonia p_(nasB) B.subtilis genome synthetic Ammonia p_(spo1)-_(tnrA1) B. subtilis genomesynthetic Ammonia p_(spo1)-_(tnrA2) B. subtilis genome synthetic ArsenicarsR/p_(arsR) E. coli plasmid native E. coli MG1655/As1 0.13 20 ArsenicarsR/p_(arsR) E. coli genome synthetic E. coli LABEC01/As3 ArsenicarsR/p_(arsR) E. coli genome synthetic E. coli MG1655/As3 0.13 33Arsenic arsR/p_(arsR) S. aureus plasmid native E. coli MG1655/As5Arsenic p_(arsR) E. coli RNA-Seq Cadmium cadC/p_(cadC) S. aureus plasmidnative E. coli MG1655/Cd1 0.04 17 Cadmium cadC/p_(cadC) S. aureusplasmid synthetic E. coli MG1655/Cd2 Cadmium cadR/p_(cadR) P. putidagenome native E. coli MG1655/Cd4 Cadmium cadR/p_(cadR) P. putida genomesynthetic E. coli MG1655/Cd3 Cadmium p_(antA) E. coli RNA-SeqChromium(VI) chrB/p_(chrB) C. metallidurans plasmid native E. coliMG1655/Cr3 Chromium(VI) chrB/p_(chrB) C. metallidurans plasmid syntheticE. coli MG1655/Cr2 Chromium(VI) chrB/p_(chrB) O. tritici transposonnative E. coli LABEC01/Cr5 5 5 Chromium(VI) chrB/p_(chrB) O. triticitransposon native E. coli MG1655/Cr5 Chromium(VI) chrB/p_(chrB) O.tritici transposon synthetic E. coli LABEC01/Cr4 Chromium(VI)chrB/p_(chrB) O. tritici transposon synthetic E. coli MG1655/Cr4Chromium(VI) chrS/p_(chrS) B. subtilis genome synthetic E. coliMG1655/Cr1 Chromium(VI) p_(recN) E. coli RNA-Seq Chromium(VI) p_(sulA)E. coli RNA-Seq Chromium(VI) p_(umuD) E. coli RNA-Seq Cobalt p_(dadA) E.coli RNA-Seq Cobalt p_(hmp) E. coli RNA-Seq Cobalt p_(ilvB) E. coliRNA-Seq Cobalt p_(lipA) E. coli RNA-Seq Cobalt p_(mmuP) E. coli RNA-Seqnative E. coli MG1655/Co7 Cobalt p_(mmuP) E. coli RNA-Seq synthetic E.coli MG1655/Co8 Cobalt nmtR/p_(nmtR) M. tuberculosis genome native E.coli MG1655/Co1 Cobalt nmtR/p_(nmtR) M. tuberculosis genome synthetic E.coli MG1655/Co2 Cobalt p_(soxR) E. coli RNA-Seq Cobalt p_(tehA) E. coliRNA-Seq Cobalt p_(ygbA) E. coli RNA-Seq native E. coli MG1655/Co3 Cobaltp_(ygbA) E. coli RNA-Seq synthetic E. coli MG1655/Co4 Cobalt p_(yjbJ) E.coli RNA-Seq native E. coli MG1655/Co5 Cobalt p_(yjbJ) E. coli RNA-Seqsynthetic E. coli MG1655/Co6 Cobalt p_(yqfA) E. coli RNA-Seq CoppercueR/p_(copA) E. coli genome native E. coli MG1655/Cu1 25 65 Copper(cusS/R)p_(cusC) E. coli genome native E. coli MG1655/Cu2 Copperp_(cusC) E. coli RNA-Seq Copper p_(cusR) E. coli RNA-Seq LeadpbrR/p_(pbrR) C. metallidurans plasmid native E. coli LABEC01/Pb1 LeadpbrR/p_(pbrR) C. metallidurans plasmid native E. coli MG1655/Pb1 LeadpbrR/p_(pbrR) C. metallidurans plasmid synthetic E. coli LABEC01/Pb2 718 Lead pbrR/p_(pbrR) C. metallidurans plasmid synthetic E. coliMG1655/Pb2 Lead p_(mntH) E. coli RNA-Seq Lead p_(shtA) E. coli RNA-SeqLead p_(ybtI) E. coli RNA-Seq Lead p_(yjjz) E. coli RNA-Seq Malathionp_(cusC) E. coli RNA-Seq Malathion p_(nemR) E. coli RNA-Seq MercurymerR/p_(merR) E. coli plasmid native E. coli MG1655/Hg4 MercurymerR/p_(merR) E. coli plasmid synthetic E. coli MG1655/Hg3 0.1 20Mercury merR/p_(merR) S. aureus plasmid native E. coli MG1655/Hg2Mercury merR/p_(merR) S. aureus plasmid synthetic E. coli MG1655/HglMercury merR/p_(merR) S. marcescens plasmid native E. coli MG1655/Hg6Mercury merR/p_(merR) S. marcescens plasmid synthetic E. coli MG1655/Hg5Zinc p_(mntH) E. coli RNA-Seq Zinc p_(shtA) E. coli RNA-Seq Zincp_(yjjz) E. coli RNA-Seq Zinc p_(zntA) E. coli RNA-Seq Zinc p_(zraP) E.coli RNA-Seq

RNA-Seq results for promoter activation in E. coli MG1655 in response tosingle and multiple toxin exposures at low and high concentrations: Ouranalysis of the sequencing data from RNA-Seq experiments to determinecandidate genes that are induced upon toxin exposure consisted of threemain tasks: sequence alignment, quantification of gene expression, andidentification of differentially expressed genes. Sequence alignment:Reads were aligned to the reference E. coli K-12 substr. MG1655 genomeusing a tolerance of at most two mismatches per alignment to protectagainst sequencing errors. The alignment was performed using Bowtiesoftware, 5 which is known to be very efficient in aligning reads to areference genome. Without being limiting, Bowtie software 5 can be usedfor the alignment. Those skilled in the art will appreciate that thereare many such software programs for performing alignments.

Quantification of gene expression: The expression level of each gene wasdetermined as a function of the number of aligned reads mapping to thegene. After analyzing several approaches adopted in the literature totabulate the number of reads mapping to each gene, we implemented ourown software capable of reproducing the counting algorithms behind someof the standard toolboxes such as Bedtools6 and HTSeq.7 In particular,we counted the number of reads mapping to each gene regardless ofwhether the read mapped to several genes, taking into account thestrand-specificity of each read. Additionally, we implemented our ownalgorithms for sequence alignment and quantification of gene expressionin order to crosscheck all results.

Identification of differentially expressed genes: Finally, a set ofstatistical and information theory algorithms were applied in order toextract not only differentially expressed (DE) genes for each toxin withrespect to the control samples (pure water) but also toxin-specificgenes. DESeq is a standard tool for identifying DE genes that allowed usto select sensitive genes with differential expression between thecontrol samples (pure water) and the cells exposed to toxin. It assumesthat the number of counts for each gene across experimental replicatesfollows a negative binomial distribution (8, 9). We considered geneswith a False Discovery Rate (FDR) lower than 1% as DE in order to ensurestatistically robust DE genes. We note that some genes showed highvariability in the control samples across different batches of RNA-Seqexperiments, indicating that these genes are very sensitive toenvironmental conditions. We identified 846 of these genes by performinga DESeq differential analysis (FDR<1%) between the control samples indifferent batches and subsequently removed them from the candidate pool.The number of DE genes (FDR<1%) identified for each condition whencompared to the negative samples in the same batch and after removinggenes that are DE between control samples is given in Table 6 (below).Ideally, good candidate specific genes are those with a significantfold-change with respect to the control samples but with a negligiblefold-change with respect to the other toxins. Additionally, genes withthe largest number of counts and expression levels are preferable inorder to maximize the signal-to-noise ratio. When it is not possible tofind toxin-specific genes, the next generation of good candidates isformed by those genes satisfying the above properties for a small subsetof toxins (multiple-toxin response). It is desirable to havesingle-toxin-specific genes for several of the toxins in the combinationin order to determine toxin-specific multi-gene-responses by means oflogical operations. In rare cases there are shared genes differentiallyexpressed. Therefore, we have developed information theoretic measuresto improve the toxin separability. The core idea of the approach is thatlow entropies (or highly informative genes) correspond to toxin-specificgenes, while large entropies (low information) are associated withscenarios in which DE fold-changes across different toxins are similarand should be discarded. The result of the analysis shows that toxinscan very easily discriminated by using simple boolean rules.

Results

We have analyzed our sequencing data and were able to identifysignificant numbers of candidate genes for each toxin of interest atfalse discovery rates below 1% (see FIG. 1). Note that unlike for othertoxins the gene identified for ammonia is down-regulated in response tothe toxin, allowing construction of a “lights-off” sensor. See, FIG. 2.This is desirable because unlike the other water toxins of interest,ammonia is expected to increase the growth rate of biosensor cells. Inthis situation, a “lights-on” sensor could be triggered by any substancethat increases the growth rate and resulting GFP production ability ofcells, whereas a “lights-off” sensor remains immune to this effect.

We have cloned all identified promoters into a standardized plasmidexpression system (see FIG. 3). The malathion sensor strain in wasobserved to accumulate high levels of GFP in some cells even beforeinduction, likely due to leaky expression of the sensor construct. Wehave designed constructs where the responsive promoter has been placedon a lower copy plasmid as well as a version with a weaker promoterwhich we expect will alleviate the metabolic burden on the cells. Inaddition, we have identified the likely transcription factor responsiblefor regulating this promoter (NemR) and designed constructs overexpressing this protein. The set of validated constructs shown in FIG. 3contains one construct that is sensitive to each toxin of interest.

Method to freeze-dry cells allowing them to be rehydrated with littleloss of viability: We have successfully developed a method for in-chiplyophilization and revival after long-term storage. A range ofcryoprotectants suitable for engineered biosensor strains and formicrofluidic geometries was formulated from a combination ofliterature-based protocols, current industrial practices, andexperimentation. The investigated cryoprotectants include:

1. 2.5% Luria-Bertrani Broth (LB) (w/v)+spectinomycin

2. 2.5% LB+0.4% glucose (w/v)+spectinomycin

3. 2.5% LB+0.4% sucrose (w/v)+spectinomycin

4. 2.5% LB+0.4% trehalose (w/v)+spectinomycin

5. M9+0.4% glucose+spectinomycin

E. coli biosensor strains E. coli MG1655 (ATCC 700926) and LABEC31 andB. subtilis biosensor strain LABBS31 were grown overnight to stationaryphase and sporulation phase, respectively. The strains were then doublewashed in cryoprotectant and concentrated to 50→ι their batch cultureconcentration. After lyophilization in a commercial freeze dryer for 12h, the strains were stored in anaerobic, nitrogen-flushed, desiccated,and opaque packaging at room temperature to protect from oxidation.

In some embodiments, a method for making a cell for determination of atoxin provided. The method can comprise delivering a nucleic acid to acell, wherein the nucleic acid comprises a sequence set forth in any oneof sequences comprising the sequences set forth in SEQ ID NO:'s 1-43. Insome embodiments, the cells are grown in a minimal media culture. Insome embodiments, the minimal media comprises glycerol. In someembodiments, the cells are frozen at −80 C.

Relative cryoprotectant efficacy was determined via plate reader revivalexperiments performed 24 h, 1 wk, 2 wk, 4 wk, and 8 wk afterlyophilization. Cells were revived via rehydration and resuspension in200 μl of revival medium within microplate wells. The plates were thenimmediately placed into a Tecan Infinite M200 Pro plate reader, wheregrowth rates were monitored over the next 48 h.

Revival media included:

1. M9+0.4% glucose+spectinomycin

2. Trace Select M9+0.4% glucose+spectinomycin

3. Trace Select M9+0.4% glucose

4. HM9 (nitrate)+0.4% glucose

and were selected to be representative of the growth media used in thefinal device.

Strains protected with optimal cryoprotectants showed little differencein viability between cryoprotectants after two months of preservation.Both E. coli and B. subtilis strains responded similarly tolyophilization in the cryoprotectants listed above (see FIG. 4). Thebest cryoprotectants, including LB+0.4% glucose and LB+0.4% sucrose,were used to perform on-chip lyophilization and have successfullydemonstrated shelf-lives of at least two weeks. Microfluidic revivalsare ongoing at the time of writing and a maximum shelf-life has yet tobe determined.

Revival occurred in Trace Select M9+0.4% glucose+spectinomycin, HM9(ammonia)+0.4% glucose, and HM9 (nitrate)+0.4% glucose via de-gas drivenchip wetting and subsequent gravity- or pump-driven flow. Initial signsof revival occur on time scales equivalent to those in the plate reader.

In order to independently load cryoprotected strains, 16- and 18-strainchips were designed and constructed with independent loading ports andcell reservoirs. After each strain is injected into its unique,fluidically-isolated reservoir, the entire device is lyophilized. Theloading ports are then sealed with a fast-curing silicone elastomer(Sylgard 170, Dow Corning). The chips are preserved and packaged usingthe same method as described above.

A biosensor chip loaded with pLBAmm3 (LABBS31), lyophilized with optimalcryoprotectants, and protectively packaged according to the protocolabove was transported and exposed to rough conditions in the high MojaveDesert for 48 h, followed by storage indoors at room temperature for anadditional 72 h. Temperatures to which the chip was exposed ranged fromnear-freezing up to 35° C. All reservoirs containing this lyophilizedammonia sensing strain revived following the introduction of medium.

Additionally, the ammonia-sensing B. subtilis LABBS31 strain has beensuccessfully sporulated using standard sporulation medium, which offersan additional preservation method with extreme shelf-life.

Deposition technique to place cells into a region of a microfluidicdevice that is then bonded to a glass coverslip: We have successfullydeveloped a deposition strategy whereby individual biosensor strains areinjected into on-chip reservoirs, where they can then be lyophilized andrevived after storage. These modifications originated from finiteelement modeling of variations of the original gill device.

To independently culture multiple biosensor strains, 16-strain and18-strain chips were designed, built, and successfully tested formulti-strain loading and freeze-drying (see FIG. 5A). The chipdimensions conform both to the demands of the biosensor's opticaldetection systems and strain requirements. The independent loadingtechnique has been successfully tested with this device (see FIG. 5B).

An air-drying and chemical bonding-based method is currently beingdeveloped in parallel with the port-loading technique to reduce thenumber of required fluidic connections to the biosensor device.

Low cost optical methods development. We have successfully developed alow cost optical system. We further have determined that bioluminescentsystems can be significantly more sensitive than fluorescence basedsystems. We have built an imaging system using the “Chameleon” camera(part #CMLN-12S2M-CS) from Point Grey Research Inc. with the designassistance of the Ziva Corporation. This is a 1.3 megapixel monochromecamera featuring a Sony ICX445 CCD imager. It contains a 12-bitanalog-to-digital converter with a maximum gain of 24 dB. The camerapackage includes a software development kit (SDK), known as FlyCapture,which is compatible with the PandaBoard single board computer systemthat we have chosen for our electronics platform. The microfluidic“gill” chip was used to compare the optical system developed by the ZivaCorporation with our research grade microscope, an Olympus IX81. TheZiva optical system was designed with lower resolution optics comparedto those of the 4× objective on the Olympus in order to lower cost whileincreasing the image field of view by 20× for imaging multiple “gill”trapping regions. Images acquired with the Ziva optics compare favorablywith those acquired with the Olympus, as shown in FIG. 6. Notably, theZiva optics are about 2× more sensitive at detecting GFP than theOlympus optics (SNRs are 23.8 and 11.6, respectively). Because ourprimary objective is to detect weak signals, the Ziva system outperformsthe Olympus system at about 50× lower cost.

Completed device prototype: We have assembled the individual componentsinto a functional prototype that is capable of acquiring and processingdata. Images of this prototype are shown in FIG. 7. The prototype iscontained in a 16″×14″ fiberglass enclosure (FIG. 7A) with an aluminumfront panel designed to protect the interior components from theenvironment. A proportional-integral-derivative (PID) temperaturecontroller is visible in the upper left hand corner of the front panelwith the peristaltic water pump in the lower right corner. The interiorof the prototype is shown in FIG. 7B with the electronics sub enclosure(FIG. 7B-1) and Ziva optical system clearly visible (FIG. 7B-5). The useof a PID controller rather than a simple thermostat is necessary forprecise control of the enclosure's internal temperature to within 0.1°C. of the set point.

Individual components are numbered 1-8. Briefly, the electronicsenclosure (FIG. 7B-1) contains the hardware for controlling the Zivaoptics, processing images and transmitting data. This system can alsocommunicate with the temperature controller (FIG. 7B-2) to adjust theinterior temperature of the enclosure (normally kept at 37° C.) usingthe Modbus protocol. This version of the prototype is designed to bepowered from a 120 VAC source and the AC power distribution block, alongwith a supplemental protection circuit breaker and solid state relay forcontrolling the heater, is shown in FIG. 7B-3. A tri-voltage powersupply, outputting 5, 12 and 24 VDC for the electronics and pumps isshown in FIG. 7B-4. The Ziva optical system for acquiring image data isshown in FIG. 7B-5. The heater is combined with a circulating fan todistribute warm air throughout the enclosure (FIG. 7B-6). Briefly, theuser inputs the desired temperature through a custom software packagedesigned by our group that runs on the PandaBoard system (FIG. 9B-1).The PandaBoard communicates with the PID controller (FIG. 7B-2) to setthe desired temperature using the Modbus protocol. The PID controllermodulates the heater's activity based on the set-point temperature andthe current temperature of the enclosure. To regulate the heater'soutput, the PID controller generates a pulse wave signal that drives theactivity of a solid state relay (FIG. 7B-3), which turns the heater's ACpower source on and off (Note: the fan remains constantly on tocirculate air). FIG. 7B-7 shows the DC power distribution system for theelectronics and peristaltic water pump (FIG. 7B-8).

To mount the necessary electronics for acquiring and processing data andto protect them from water exposure, we designed a custom sub-enclosureusing Solidworks (Dassault Systems) and had it fabricated using additivemanufacturing (3D printing) by a local machine shop (FIG. 7C). Adepiction of the Solidworks representation of this enclosure, showing anArduino Uno (FIG. 7C-1), BuckBlock LED drivers (FIG. 7C-2), PandaBoardsystem on a chip (FIG. 7C-3) and LED control relays (FIG. 7C-4) is shownin this Figure. The PandaBoard communicates with the Arduino over aRS-232 (serial) link to modulate the LED control relays. The Arduinothen generates a pulse wave modulated output signal which is interpretedby the BuckBlock LED drivers to control the brightness of the LEDs (onefor both transmitted light and GFP excitation). The PandaBoard is thenresponsible for acquiring an image from the Ziva optical system (FIG.7B-5) and finally signaling for the LEDs to be turned off. The acquireddata is analyzed on the PandaBoard and the results are transmitted via asecure Wi-Fi link to our data repository server. The software to do thiswas custom programmed in Java and C using the Point Grey Fly-Capture SDKand implemented on a PandaBoard ES rev B.3 running Ubuntu Server. Thevarious components of the Ziva optical system are shown in FIG. 7D,including: the transmitted light optics (FIG. 7D-1), the microfluidicdevice stage and thermistor temperature probe (FIG. 7D-2), the focusadjustment system (FIG. 7D-3), the dichroic mirror holder (FIG. 7D-4),the GFP excitation system (FIG. 7D-5) and the Point Grey Chameleonmonochrome camera (FIG. 7D-6). An image of a microfluidic device,illuminated with the Ziva GFP excitation optics is shown in FIG. 7E withexample images shown in FIG. 7F (top transmitted light, bottom GFPfluorescence).

Computational models to determine the threshold of detection forspecific sensors based on models of experimental GFP responses. We usedmachine learning techniques to determine the relationships between theGFP output signal and the presence of a toxin. We have created adatabase containing the collected sensor response data. We havequantified the GFP threshold of detection for each sensor construct. Wehave constructed Receiver Operating Characteristic (ROC) curves for eachsensor to achieve robust sensing. We constructed machine learning modelscapable of inferring the relationships between the GFP sensor responsesand the presence or absence of a toxin at a given concentration. Thealgorithm learns these relationships from a set of training samples (GFPsensor responses) defined by the set of experimental conditions fromwhich they were generated. The aim of the algorithm is to provide ageneral method capable of determining the experimental conditionsassociated with GFP sensor responses through the use of historical data.Specifically, we have built classification models based on SupportVector Machines (SVMs), which is one of the most popular classifiers dueto its excellent performance in many contexts and its solid mathematicalbasis (1). For each toxin and concentration, we solved a binaryclassification problem in which the positive class represents thepresence of the toxin in water and the negative class is associated withclean environments. Patterns were constructed with features containingGFP sensor responses at various timestamps to capture the temporaldynamics of the GFP signal. The optimal meta-parameters of the SVMclassifier were determined by applying a 5-crossvalidation during thetraining phase (2). In order to have a reliable estimate of theperformance of the model when deployed in real environments, we measuredits performance over a set of samples (test patterns) not seen duringthe training phase. The SVM's performance was determined by thepercentage of test samples correctly labeled as toxin/no toxin(classification accuracy). 80% of samples were used for training the SVMmodels and the remaining 20% of samples was used to evaluate theireffectiveness. We generated 20 random training/test partitions to havean estimate of performance independent of the data partition. Table 4shows the average classification accuracy over the test set obtainedacross the 20 random partitions for each binary classification problem.

TABLE 4 Classification accuracy results (Acc) obtained from the GFPsensor responses for different toxins at different concentrations(Conc.). Toxin Conc.(μM) Acc (%) As 0.2 97.35 (pRS18) 0.55 100 1 100 As0.1 97.35 (pZA47a) 0.5 100 1 100 Cd 0.022 65.00 0.044 54.16 0.44 90.00Cr 1.25 96.67 2.5 97.50 5 94.17 Toxin Conc.(μM) Acc.(%) Cu 5 95.00 1096.67 20 95.00 Hg 0.2 63.33 1 87.50 2 99.17 Pb 1.8 95.83 3.6 95.00 7.295.00 Malathion 72.5 mg/L 87.50 145 mg/L 89.17 NH4+ 1 ppm 71.05 5 ppm91.79

Based on the results presented in (Table 4) and the ROC curves (FIG. 8),the models establish the threshold of detection at 0.2 μM for Arsenicusing the plasmid pRS18, 0.1 μM for Arsenic using pZA47a, 0.44 μM forCadmium, 1.25 μM for Chromium, 5 μM for Copper, 1 μM for mercury, and1.8 μM for lead. Malathion can be detected when diluted at 72.5 mg/L andammonia can be detected at concentrations of one particle per million.

Construction of the Receiver Operating Characteristic curve (ROC) forthe sensors to minimize false negatives and false positives): Theresults in were obtained by assuming that the penalties ofmisclassification are identical for positive and negative classes. Inother words, the cost of classifying a GFP signal as “toxic” when it isnot (or vice versa) is the same. However, it may be the case that thecost is not symmetric for positive and negative cases. A water sensor isa good example of this situation since it might be preferable to ensurehigh accuracy when toxins are actually in the water (true positive rate,TP) in exchange for increasing the number of cases that are classifiedas “toxin present” when there is not any toxin in the water (falsepositive rate, FP). The Receiver Operating Characteristic (ROC) curve is2-D parametrized curve used to quantify and represent the tradeoffbetween the true positive rate and the false positive rate of a givenclassifier. The abscissa represents the False Positive rate, while theordinate shows the True Positive rate. Therefore, the optimal classifieris represented by a point in the upper left corner of the ROC curve,since this point corresponds to the best possible case in which theclassifier is able to correctly identify 100% of positive cases (toxinpresent) with no false alarms. The parameter that defines the ROC curvein our classification model is the decision threshold, which determineswhether a pattern (GFP signal) is classified as positive (toxin present)or negative (toxin not present). The SVM model provides a value(decision function) for each pattern that represents the confidence ofthe model in its prediction, and the final classification is obtained byassigning to the negative class those points with decision functionsthat are below the decision threshold, and classifying as positivesamples those patterns with decision functions above this threshold.Therefore, by sweeping a grid of possible values for the SVM decisionthreshold, we obtained the ROC curves for the different toxins shown inFIG. 8.

Design the controller board with wireless capability: We have chosen toimplement a single board computer that contains wireless Ethernetcapability. We tested two low-power platforms based on the TexasInstruments ARM processor with wireless capability: one based on theSitara ARM and the other based on the Cortex-A9 (see FIG. 9). We haveopted for the more powerful Cortex-A9 due to the ease of use and lowpower consumption. We are currently using a PandaBoard, which is poweredby a Texas Instruments OMAP4430 system on chip (SoC) device. TheOMAP4430 chipset contains a dualcore 1 GHz ARM Cortex-A9 MPCore CPU with1 GB of DDR2 SDRAM, wireless Ethernet capability, and an SD card slotoffering up to 32 GB of storage. The electronics are similar to a modernsmartphone in terms of processing power and power consumption. ThePandaBoard solution allows us to install a Linux operating system sothat we can use standard gnu compilers and run our software without anymajor modifications, which we demonstrate in the next two aims.

Reduce the size of the pattern recognition algorithms to be able to beembedded in the PandaBoard. Fluorescence images are used to train ourclassifier to be able to detect and discriminate between differenttoxins. In order to speed up the operation of the classifier we mustreduce its computational cost, which is directly linked to the number ofimages and the number of features in each image. Each image consists ofa set of numerical features, each containing the intensity of a pixel inthe image. In FIG. 10, we show examples of images captured by sensorprototype. Each image has a resolution of 1280×960 pixels, which meansthat each pattern is defined by 1, 228, 800 features. This is a largesize for a pattern recognition algorithm, especially if it is intendedto run in the PandaBoard. Fortunately, the images contain severalirrelevant features both near the edges and center of the image (seeFIG. 10A), and they are also likely to have a large number of redundantfeatures in neighboring pixels. Therefore, it is extremely useful toapply a feature selection algorithm to find the most informativefeatures and to reduce the computational cost of the classifier. To thisend, we implemented a Quadratic Programming Feature Selection (QPFS)algorithm that takes into account both the relevance and redundancy offeatures and has been shown to be an effective approach for selectingfeatures in images. We generated a dataset formed by 288 images capturedwith GFP signal in one of the chambers in the presence of Arsenic (FIG.10B), and we rotated these images at 16 different angles to simulatedifferent responses of the chambers to multiple chemical inputs. Theimages were compressed by a factor of 16, and images were padded withblack pixels in order to create square images after rotation. Theresolution of the resulting images was 114×114, yielding a final datasetformed by 288×16=4, 608 images in a 114×114=12, 996 dimensional space.QPFS was applied to this data using the Pearson's correlation betweeneach pixel as a measure of redundancy and the toxin signature as ameasure of relevance. The QPFS assigned a weight to each pixel between 0and 1, and features with larger weights were considered better variablesto use for classifier training. The result of the QPFS algorithm ispresented in FIG. 11A, which shows how the feature selection process isable to detect the underlying structure of the images. Additionally,FIGS. 11B-D show the first 100, 200, and 500 features selected by QPFS,respectively. According to these preliminary results, 500 features isenough to recover the discriminative information in the images. Thisrepresents a reduction in the dimensionality of the problem by afactor>2, 400 (1, 228, 800/500), which enables the integration of thepattern recognition algorithm in the PandaBoard.

We successfully compiled the software, trained the model, and ran thetrained model. The PandaBoard can classify an image in real time in anaverage of 0.05 seconds. The algorithms were developed using the open MPsoftware and compiled using gnu g++. Both are well established andstable options that run perfectly fine on the PandaBoards. Weimplemented aggressive compiler optimizations that produce fast nativecode on the ARMs. The multicore Cortex-A9 processors proved to beextremely fast and sufficient to run all of the algorithms.

Luminescent reporters. We have investigated replacing the standard GFPreporter with one based on bacterial luciferase (Lux), and we havedemonstrated that an arsenic sensor plasmid modified in this way showsincreased sensitivity, likely due to eliminated autofluorescence. Infact, data provided herein shows that a bioluminescent sensor is moresensitive than many analytical methods approved by the EPA for testingarsenic in drinking water, demonstrating the power of our techniques. InFIG. 8, we compare these reporters driven by an arsenic-responsivepromoter, where the induction response was measured side-by-side using aTecan plate reader.

The background noise was significantly lower for the Lux construct,allowing detection of arsenic at a concentration of 0.2 nM; the GFPconstruct appears to be sensitive to arsenic only above the 5.2 nMlevel. For comparison, the arsenic concentration used for testing theGFP construct in a microfluidic device was 130 nM, or 0.13 μM. Thus,replacing GFP with Lux promises to greatly increase the sensitivity ofthe biosensor.

Table 5 shows specificity results for toxin-sensing plasmids identifiedfrom the literature and cloned into synthetic constructs expressed in E.coli. Here, strains representing the most sensitive promoters from ourmicrofluidic induction experiments were grown in the wells of amicroplate in the presence of on- and off target heavy metals. Eachcolumn represents the fluorescence response of a strain to the on-targetmetal (normalized to “1”) and all off-target metals, where “0”represents the unchanged response for the no-toxin control. Althoughthese promoters are generally specific to the on-target toxin, somecrosstalk is evident (i.e. Cr5 is sensitive to iron; Hg3 is sensitive tochromium(VI) and zinc; Pb2 is sensitive to several off-target toxins).Fortunately, in cases of significant crosstalk, nonspecific promoterresponses can be combinatorially combined with other promoter responsesto generate specific multi-promoter responses (see FIG. 2 and AppendixB).

TABLE 5 Strain/On-Target Toxin Conc. As3/ Cd1/ Cr5/ Cu1/ Pb2/ Hg3/ Toxin(μM) Arsenic Cadmium Chromium(VI) Copper Lead Mercury Arsenic 1.3 1.00−0.18 0.24 −0.05 0.33 0.25 Cadmium 0.44 −0.13 1.00 −0.02 −0.08 0.46−0.26 Chromium(VI) 2 −0.22 0.62 1.00 −0.06 1.69 −0.96 Copper 20 −0.09−0.08 1.00 −0.17 0.16 Lead 1 −0.05 0.06 0.01 −0.05 1.00 0.12 Mercury 0.1−0.18 −0.09 0.58 −0.05 1.44 1.00 Cobalt 20 0.30 0.67 −0.11 −0.01 0.80Iron 500 −0.06 −0.31 0.86 0.17 0.96 −0.38 Nickel 1.7 −0.08 −0.08 −0.35−0.05 0.16 Zinc 7.6 −0.14 −0.09 0.35 −0.08 1.06 0.78

Table 6 shows differentially expressed (DE) genes at a false discoveryrate below 1% for each condition of interest.

TABLE 6 Condition Number DE Arsenic low 3 Cachnium low 132 Cliromium(VI)low 270 Cobalt low 609 Copper low 110 Iron low 32 Lead low 99 Malathionlow 370 Mercury low 165 Nickel low 43 Zinc low 24 Arsenic high 14Cadmium high 38 chromium(VI) high 377 Cobalt high 1274 Copper high 77Iron high 70 Lead high 489 Malathion high 41 Mercury high 218 Nickelhigh 95 Zinc high 596

Alternative 2 (Task A)

Milestone 1: Create an initial library of transcription based sensors. Amajor goal is to identify a combination of cellular signals that willindicate the detection of specific targeted chemical agents. In order toaccomplish this goal, an initial library of transcription based sensorsis created and then the library of the transcription based sensors isexpanded using Next Generation Sequencing (NGS) techniques. What isdeliverable is a comprehensive list of list of candidate genes in E.coli that respond to target compounds. We obviated the need tointermediately test the response of the expression constructs in batchculture using a plate reader in Milestone 1.3 by directly validatingwithin custom microfluidic devices in Milestone 1.4.

Create an initial library of transcription based sensors. A list oftoxin responsive promoters based on literature research was constructedin milestone 1.1. Promoters were assembled from multiple bacterialspecies along with necessary regulatory genes into synthetic expressionconstructs. The need to intermediately test the response of theexpression constructs in batch culture using a plate reader was obviatedby directly validating within custom microfluidic devices.

Milestone 1.1. Identify 33 promoters for each toxin of interest based onliterature searches: 3 or more promoters from literature for each toxin,when available, were identified. The exceptions are copper (2), lead(1), and malathion (0), for which less than three promoters are known.

Bacteria that grow in environments with high toxin levels, occurringeither naturally or as a result of pollution, have evolved pathways tomitigate their effects on cellular metabolism. These resistance pathwaysare often activated by a specific transcription factor that is sensitiveto the intracellular concentration of the toxin. The scientificliterature was searched to identify well-characterized transcriptionfactor/promoter pairs. Where divergent pathways responding to the sametoxin have been characterized, at least one representative pathway fromeach major evolutionary clade was chosen. Identified candidateregulatory mechanisms for specific toxins, including the native organismand naturally-occurring DNA construct, are provided below.

Arsenic: The arsR family of transcription factors contains manyarsenic-sensing members, including one found on the E. coli genome andanother encoded on the E. coli R773 plasmid. These two candidates wereselected for synthesis because they are native to E. coli and have beenused previously to construct a biosensor. We also synthesized an arsRconstruct from S. aureus.

Cadmium: Cadmium-responsive transcription factors have been identifiedfrom both the arsR family and the merR family of transcriptionalregulators. A representative member from each group: cadC from S. aureusand cadR from P. putida was selected.

Chromium(VI): The chromate-responsive element from O. tritici wasselected, which has been characterized and used to construct a highlyspecific biosensor, a related system from C. metallidurans, and anunrelated chromate-responsive transcription factor from the genome of B.subtilis NCIB 3610.

Copper: Two well-studied functionally-unique copper-responsive elementsnative to E. coli were selected. CueR functions as anactivator/repressor of the merR family, while cusS/R is a two-componentsensor system including a histidine kinase, which may aid in signalamplification.

Lead: The only lead-specific system identified in the literature ispbrR, found on a mega plasmid in C. metallidurans.

Mercury: Mercury-inducible merR systems have previously been used toconstruct biosensors with low detection limits, where merR functions asa repressor in the absence of mercury and an activator in the presenceof mercury. The same strategy for our biosensor plasmids was adapted,selecting three well-studied members of the merR family. Another moreevolutionarily divergent merR protein from S. lividans was not pursuedbecause it was shown to function only as a repressor.

Ammonium: Sensing ammonium requires culturing cells in a backgroundnitrogen source that is less-preferred than ammonium. While nitrate isthe most suitable background nitrogen source for this purpose due to itslong-term stability in solution, E. coli MG1655 is unable to assimilateit under aerobic conditions. Therefore, it was decided to sense ammoniumusing the bacterium B. subtilis NCIB 3610, which can utilize bothnitrate and ammonium as a nitrogen source. The native ammonium-sensitivepromoters pnasA and pnasB and the synthetic promoter p_(spo1-tnrA) wereincorporated into plasmids for integration into a neutral site on the B.subtilis NCIB 3610 genome, where they act as a second copy of thepromoter to drive GFP expression in the presence of ammonium.

For completeness, in Table 1 the full list of identified candidateconstructs is presented. Sensor plasmids containing the most promisingregulatory candidates (shown in boldface in Table 1) for arsenic,cadmium, chromium(VI), copper, lead, and mercury were synthesized by thecloning vendor and ported into E. coli to drive GFP expression in thepresence of the toxin. Synthesized sensing constructs for ammonium wereintegrated into a modified version of the B. subtilis NCIB 3610 hoststrain, where a motility gene (hag) and a biofilm pathway gene (epsH)were knocked out for improved growth within microfluidic devices.

In some embodiments, a method of making a nucleic acid for detection forthe presence or levels of an analyte in an aqueous sample is provided.The method can comprise attaching a promoter to a reporter protein,wherein the promoter is specifically turned on by ammonia, arsenic,cadmium, chromium (VI), cobalt, copper, lead, malathion, mercury, andzinc. In some embodiments, the reporter protein is a fluorescentprotein, such as GFP.

Milestone 1.2: Clone the promoters into an expression plasmid drivingthe production of GFP: All promoters identified from the literature werecloned into a standardized plasmid expression system. Two standardizedplasmid backbones for testing the candidate toxin-responsive elementsidentified in the literature in E. coli were synthesized. Both vectorsinclude the p15A medium-copy origin of replication, a spectinomycinresistance cassette for selection of positive transformants, and apromoter-less GFP insulated by flanking terminators. One of the vectorsreplaces the native ribosome binding site (RBS) in front of GFP with aversion known to produce high levels of expression in E. coli MG1655.

Both synthesis of the plasmid backbones and insertion of the sequencesfor candidate toxin-responsive elements into each version were carriedout by Transcriptic. The sequence of interest was inserted such that thetranscriptional regulator remains under the control of its nativepromoter, with the inducible promoter driving expression of GFP. Becauseall DNA constructs were completely synthesized, we were able to codonoptimize sequences for improved heterologous expression in E. coliMG1655.

Those skilled in the art will appreciate that gene expression levels aredependent on many factors, such as promoter sequences and regulatoryelements. Another factor for maximal protein selection is adaptation ofcodons of the transcript gene to the typical codon usage of a host. Asnoted for most bacteria, small subsets of codons are recognized by tRNAspecies leading to translational selection, which can be an importantlimit on protein expression. In this aspect, many synthetic genes can bedesigned to increase their protein expression level. The design processof codon optimization can be used to alter rare codons to codons knownto increase maximum protein expression efficiency. In some alternatives,codon selection is described, wherein codon selection is performed byusing algorithms that are known to those skilled in the art to createsynthetic genetic transcripts optimized for higher levels oftranscription and protein yield. Programs containing algorithms forcodon optimization are known to those skilled in the art. Programs caninclude, for example, OptimumGene™, GeneGPS® algorithms, etc.Additionally synthetic codon optimized sequences can be obtainedcommercially for example from Integrated DNA Technologies and othercommercially available DNA sequencing services.

Milestone 1.3: Validate the response of the promoters using traditionalbatch experiments (Note validation indicates a ratio of inducedfluorescence signal to uninduced of at least 3:1): Instead of performingpreliminary testing in batch, we validated the response of the promotersdirectly in our microfluidic devices.

It was expected that the development of the microfluidic devices wouldlag the construction of our toxin-sensing plasmids, thereby requiringthat initial induction experiments be performed using a fluorescentplate reader. However, rapid development of the microfluidics allowedrunning of initial induction experiments of the sensing strains on chip,and, upon successfully inducing sensing strains on-chip, as such therewas no need to replicate experiments in batch culture for severalreasons. First, since cellular measurements are highly dependent upongrowth phase, and batch culture cannot provide a constant growthenvironment, it is expected to have more reproducible results from themicrofluidic chemostats. Second, inducing on-chip can allow one todynamically control the inducer concentration and thereby moreefficiently scan the induction range for each sensing strain. Finally,the deployable biosensor device will incorporate microfluidic culturingand optical imaging; therefore, microscope imaging on-chip is morerelevant than reading fluorescence values within the batch culture wellsof a plate reader.

Milestone 1.4: Validate the response of the promoters using microfluidicdevices. Quantitatively measure GFP signal in response to variousrelevant levels of toxins of interest: The response of the promoters inmicrofluidic devices for all toxins of interest have been validated.

A microfluidic device to culture and induce each sensing strain withvarying levels of toxins of interest was developed. Termed the “gillchip,” this device is a variation on the “biopixel” device previouslydeveloped in the lab. Structurally, this microfluidic device consists ofa polydimethylsiloxane (PDMS) elastomer block with recessed channelsthat are sealed upon bonding to a glass cover slip. Fluidically, thecore functional unit of the device is a microfluidic channel with long,narrow branches along the sides that serve to retain cultures of cells(FIG. 45). The precise geometry of the “cell trap” createshigh-resistance, low-flow regions where cells grow in a defined areawith continuous perfusion of fresh medium. Previous studies of syntheticgene circuits required microfluidic devices which grow cells in amonolayer in order to perform single-cell tracking. The gill chip uses asimilar side trap geometry, but the trap width is decreased from 100 mmto 10 mm, and the trap height is increased from 1.65 mm to 50 mm. Byusing thin, tall traps, the optical signal is increased whilemaintaining the ability to selectively grow cells in a low-flow trapregion. The overall device consists of an array of trapping unitsconnected in parallel, each supporting the long-term culturing of a cellstrain. The central channels of the trapping regions are fed by a singleswitchable medium source.

The length of the cell trapping channels of the gill chip was optimizedto maximize fluorescent signal while ensuring adequate medium deliveryfor healthy cell growth. Medium delivery to the packed cells in thetrapping channels is limited by diffusion, and it was observed thatexcessively long channels slowed cell growth and GFP production. Theoptimal trap length was determined by fabricating multiple versions inparallel and measuring expressed fluorescence from a preliminaryarsenic-sensing strain. In FIG. 46, it was seen that a 120 μm channellength produces the largest fluorescent signal for arsenic levelsranging from below 1× to 10× the EPA limit. Therefore, our trap lengthwill be standardized in future experiments at 120 μm. It was confirmedthat representative gram-negative (E. coli MG1655, S. Typhimurium) andgram-positive (B. subtilis NCIB 3610) bacterial strains as well as yeast(S. cerevisiae MFSC120) form stable cultures in the gill chip with120-μm-long trapping channels. Initial experiments continuously growingE. coli MG1655 in LB medium in the gill chip demonstrated a devicelifetime of 27 days before flow stopped due to channel clogging. Thislifetime was later extended to 50 days by slowing the cellular growthrate by replacing LB medium with M9 minimal medium.

While waiting for Transcriptic to synthesize the initial library oftoxin-sensitive plasmids based on our literature search, themicrofluidic device was used to measure the response of twotoxin-sensing promoters in E. coli MG1655 generated in the lab forprevious work.

Induction of pRS18, an arsenic-sensing plasmid, was first tested in themicrofluidic device. A recombinant strain of E. coli MG1655 containingpRS18 was vacuum loaded into the microfluidic device, grown toconfluence, and step-induced with sodium ortho-arsenite (Na3AsO3) in M9minimal medium supplemented with 0.4% glucose. Images were collectedusing a Nikon Ti microscope with 4× objective magnification. Stepinduction was executed using the gravity-driven “Dial-AWave” automatedflow control system to mix two medium sources in a defined ratio. Thecells were sequentially exposed to arsenic concentrations of 0 μM, 0.1μM, 0.5 μM, and 1 μM in 6-h windows, allowing time for the cells torespond to each concentration increase. The data shows a significantdetection of 0.1 μM arsenic, which is below the EPA limit of 0.13 μM.FIG. 47 shows the trapped cells prior to (panel a) and following (panelb) 6 h of induction at 0.1 μM arsenic. We believe that the rise in GFPexpression prior to the induction step is due to a small leak of inducerinto the medium stream. Average fluorescence within the trapping regionsof the images as well as arsenic concentration is plotted over time inFIG. 47C.

Second, induction of pCue-CopA, a copper-sensing plasmid, was tested inthe microfluidic device. A recombinant strain of E. coli MG1655containing pCueCopA was vacuum-loaded into a microfluidic device, grownto confluence, and step-induced with copper sulfate (CuSO4) in LBmedium. Images were collected using an Etaluma LumaScope microscope with20× objective magnification. Step induction was again performed usingthe “Dial-A-Wave” automated flow control system. The cells weresequentially exposed to copper concentrations of 0 μM, 25 μM, 52 μM, 77μM, and 99 μM in 6-h windows. FIG. 48 shows a significant detection of25 μM copper, which is slightly above the EPA limit of 20.5 μM.

A literature-based library of synthesized sensor plasmids fromTranscriptic was received and transformed into both E. coli MG1655 andE. coli LABEC01 (See Detailed Methods for strain details) for on-chiptesting. To increase the throughput of the microfluidic experiments, aversion of the gill chip with four independently loadable cell growthareas for culturing four sensing strains in parallel was developed (seeFIG. 49). These growth areas are fed from the output of an on-chip“Dial-A-Wave” system to dynamically control the local toxinconcentration.

For on-chip induction experiments, strains were loaded into the deviceand grown in M9 minimal medium with 0.4% glucose and 50 μg/mlspectinomycin (for plasmid maintenance) for 2 days in the absence oftoxin. Cells were imaged under 4× brightfield and GFP fluorescencefilters on an Olympus IX-81 microscope every 2 min for 6 h under thesenon-inducing conditions to generate a fluorescence baseline. The toxinwas then introduced to induce GFP expression and the cells were imagedevery 2 min for 6 h.

To determine the response to toxin induction, the raw fluorescenceimages before and after induction were compared. The image stacks wereanalyzed using a custom ImageJ script to measure the average GFP signalover time for each cell trap. Any linear trend in the uninducedfluorescence data due to cell growth was subtracted from the data. Inthis manner, the dynamic signal-to-noise ratio (SNR) was calculated as:(current fluorescence−mean uninduced fluorescence)/(standard deviationof uninduced fluorescence).

This microfluidic platform was then used to measure the response ofseveral toxin responsive promoter constructs identified from theliterature and synthesized by Transcriptic. Table 2 summarizes theresults of these experiments, listing the most sensitive promoterconstructs exposed to each toxin and the calculated SNR values after 6 hof induction. Note that SNR values greater than 2 indicate a significantresponse.

The on-chip time-lapse induction response of each promoter construct inthis table can be seen in FIG. 36. In each figure panel, the moment oftoxin introduction is represented as a step in the red trace. While somepromoter constructs exhibit a sensitive response by highly expressingGFP immediately following induction, others do not. Note that thearsenic sensor plasmid As3 was used as a negative control forexperiments with non-arsenic-sensing strains, and it showed no inductionin the presence of other heavy metals.

To investigate the specificity of the most sensitive promoter constructfor each toxin identified from the literature, cloned into syntheticconstructs expressed in E. coli, and measured on-chip (see Table 2),these strains were grown in the wells of a microplate in the presence ofon- and off-target heavy metals. Although the ability to sense cobalt,iron, nickel, and zinc is of secondary importance, these heavy metalswere included as potential off-target inducers because they may bepresent in the natural water supply under test. Each column of Table 5shows the fluorescence response of a strain to the on-target metal(normalized to “1”) and all off-target metals, where “0” represents theunchanged response for the no toxin control. Although these promotersare generally specific to the on-target toxin, some crosstalk isevident. Fortunately, in cases of significant crosstalk (e.g. Pb2 leadsensing strain), nonspecific promoter responses can be combinatoriallycombined with other promoter responses to generate specificmulti-promoter responses (see FIG. 1 and Appendix B). This combinatoriallogic was implemented in algorithms. Additionally, theliterature-identified promoters were supplemented with highly specificpromoters via RNA-Seq analysis (e.g. ybiI lead responsive promoter).

Milestone 2: Massively expand the library of transcription based sensorsusing Next Generation Sequencing (NGS) techniques. The proposed work wasexpanded by exposing multiple strains of bacteria (in addition to E.coli) to relevant levels of all toxins and extracting RNA (Milestone2.1). To ensure that the biosensor will have high selectivity for thetoxins of interest, the sensing bacteria were exposed to multiple toxinsat once and RNA was extracted (Milestone 2.2). The need for testing atmultiple temperatures was obviated by ensuring tight temperature controlin our prototype enclosure as described heron (Milestone 2.3). Themicrofluidic devices were optimized to achieve growth rates similar to abatch culture as described herein (Milestone 2.4). Isolated RNA wasprepared and sequence data was generated as described herein (Milestone2.5). The sequencing data for all toxins and discovered numerousdifferentially expressed genes as described in the embodiments herein(Milestone 2.6).

Milestone 2.1: Expose target cells to toxins of interest over fourorders of magnitude of concentration and extract RNA: Three strains ofbacteria were exposed to relevant levels of all toxins and extractedRNA. The toxin exposure protocol involves first growing a culture ofbacterial cells to mid-log phase (OD≤0.2). Second, it was diluted withfresh media, then the toxin of interest was add, and it was cultured at37° C. for 3 h, ensuring that the cells do not exit exponential growthphase before harvesting (OD_0.25). Finally, cellular RNA was stabilizedusing Qiagen RNA Protect reagent, the cells were centrifuged, and thepellet was frozen at −80° C. To investigate the cellular RNA response totoxin insult, we exposed E. coli MG1655, E. coli LABEC01, and B.subtilis 168 cells to the toxins and concentrations shown in Table 7 andextracted RNA.

TABLE 7 Concentrations used for single-toxin exposures of bacterialstrains to investigate the cellular RNA response for sensitivity. Mostwater toxins to be sensed by our device (boldfaced) are identified bythe EPA as being of primary (P) importance. Sensing is expected to occurin the presence of interfering substances of secondary (S) importance(not boldfaced) Exposure Conc. (μM) Toxin P/S E. coli MG1655 E. coli LABEC01 B. subtilis 168 Ammonium n/a 71.4, 714 Arsenic P 0.25, 1 0.1, 1Cadmium P 0.4, 1.2 0.04, 0.4 Chromium(VI) P 0.2, 2 0.2, 2 Cobalt S 20,200 Copper P 0.2, 2, 20 0.2, 2 Iron S 5.4, 54 Lead P 0.1, 3 0.1, 3Malathion n/a 21.9, 219 21.9, 219 Mercury P 0.01, 0.1 0.01, 0.1 Nickeln/a 0.77, 7.7 Zinc S 76, 760

In determining the toxin concentrations for exposure, EPA limit wasexplored. A growth curve data for E. coli MG1655 cells exposed to eachtoxin at levels around the EPA limit was generated (FIG. 50) and,combined with our induction data, found that the concentration range ofinterest between the detection limit and cell death is generally lessthan 4 orders of magnitude. Surprisingly, toxin levels in this rangetend not to slow down growth rate but instead delay the onset of theexponential growth phase, as illustrated by copper exposure in FIG. 50A.FIG. 50B shows that the dependence of growth phase lag on copperconcentration is approximately linear. If cells exposed to a toxin atthe EPA limit did not exhibit this growth phase lag (see growth ratesfor mercury exposure in FIG. 50C), it was assumed that they were not atrisk of death and we probed additional toxin concentrations that werehigher. If cells exposed to a toxin at the EPA limit exhibited thisgrowth phase lag (see growth rates for chromium(VI) exposure in FIG.50D), it would be assumed that they were at risk of death and we probedadditional toxin concentrations that were lower. The general strategywas to examine the cellular RNA response at toxin levels where the cellsare responding specifically to the presence of the toxin withouttriggering a universal (and unspecific) cellular stress response.

Milestone 2.2: Expose target cells to multiple toxins at once andextract RNA to determine selectivity of sensor response and usecomputational algorithms to determine a highly accurate relationshipbetween cellular signals and sensor response: Cells were exposed tomultiple toxins at once and we determined that the promoters weidentified respond as expected, even in the presence of an additionaltoxin.

Six RNA-Seq experiments were performed with As+Cd, As+Hg, and Cd+Hg inE. coli MG1655 and LABEC01 to investigate the specificity of theexpression responses. Concentrations used for each multiple-toxinexposure are shown in Table 8.

TABLE 8 Concentrations used for multiple-toxin exposures of E. colistrains to investigate the specificity of the cellular RNA response.Exposure conc. (μM) Toxin Combination E. coli MG1655 E. coli LA BEC01Arsenic/Cadmium 0.25/0.4 0.1/0.04 Arsenic/Mercury 0.25/0.1 0.1/0.01Cadmium/Mercury  0.4/0.1 0.04/0.01 

For this subset of toxin combinations, only those RNA-Seq promotercandidates responding to each individual toxin also respond to the sametoxin in combination with others (See the rightmost three conditions inFIG. 51 and additional figures in Appendix B). To avoid the complexityof using this RNA-Seq approach combinatorically to investigatespecificity across all toxin combinations, it was concluded that themost efficient approach is to develop sensor strains based on RNA dataextracted following single-toxin exposures and then expose these strainsto other toxins to look for non-specific responses. Because there wereseveral strains that respond to each toxin, even for some overlap inresponse between certain promoters and multiple toxins, computationalanalysis can be used to determine a disjoint set of genes, and thereforea unique response signature, for each toxin of interest. The prototypedevice containing an array of sensing strains was exposed to each toxinand used the GFP “fingerprint” (i.e. which strains are responding atwhat intensities) to train a robust computational algorithm to decodethis fingerprint into the toxin and concentration present in the sample.

Milestone 2.3: The above discussed experiments were repeated at 25° C.,30° C., and 37° C.: All biosensing organisms grow optimally at 37° C.Upon investigating the ability to maintain a constant on-chiptemperature, the sensor was designed with temperature control, as thisimplementation will be simple and will eliminate the risk of anytemperature-related sensitivity or specificity issues.

Milestone 2.4: Determine how growth system affects geneexpression—compare batch grown cells to microfluidic grown and ensureproper growth and response of cells to toxins in device environment: Themicrofluidic device was developed and confirmed that the cells in thechambers are healthy, expressing normally, and their growth rates arecomparable to those observed in batch conditions. A simple method toindicate healthy cell growth on-chip is to compare estimatedmicrofluidic growth rate with calculated batch growth rate. E. coliMG1655 cells were grown in M9 minimal medium supplemented with 0.4%glucose in the plate reader and extracted a doubling time of 0.93 h frommeasurements of OD600 over time. The cells were grown in themicrofluidic device with the same medium and the growth rate wasestimated by collecting the effluent exiting the device, seriallydiluting it, and plating it on LB medium in agar to determine the viablecell count. Using this data, it was determined that the on-chip doublingtime is about 0.94 h, which is in excellent agreement with batch data.Certainly, the batch and on-chip growth rates are comparable, and fromour extensive microfluidics experience we observed with confidence thatthe on-chip cells are healthy and expressing normally.

Milestone 2.5: Prepare isolated RNA for Illumina sequencing using vendorprovided reagents and carry out sequencing: We have prepared librariesand carried out sequencing for all toxins of interest. After exposingbacterial cultures to various levels of toxins, we extracted thecellular RNA and prepared it for sequencing. First, we thawed the cellpellet, homogenized the pellet using bead beating with RNase-freezirconium oxide, and extracted the RNA using Qiagen RNeasy Kits. Second,we removed any contaminating genomic DNA with DNase and performed afinal purification step using a Zymo Clean and Concentrator column.Third, we prepared the RNA library for sequencing by enriching for mRNAusing Epicentre Ribo-Zero rRNA removal kits. Fourth, we generated cDNAand prepared an indexed Illumina library using NEBNext Ultra DirectionalRNA Library Prep Kits, which retain strand-specific orientationinformation. Finally, we prepared and loaded these libraries on ourIllumina MiSeq instrument for 2×75 bp paired-end sequencing.

Milestone 2.6: Analyze data to determine candidate genes which areinduced upon toxin exposure: We analyzed our sequencing data andidentified significant numbers of candidate genes for each toxin ofinterest at false discovery rates below 1%. As we explain in Milestone3.3, we experimentally validated selected candidate genes for cobaltdetection.

Our analysis of the sequencing data from RNA-Seq experiments todetermine candidate genes that are induced upon toxin exposure consistedof three main tasks: sequence alignment, quantification of geneexpression, and identification of differentially expressed (DE) genes.The established software was rigorously tested and development of thesoftware was performed to corroborate and verify all the candidate DEgenes (see Table 9). We took into account the stranding of the MiSeqreads to achieve greater than 95% alignment with the MG1655 genome.Details of our quantification of the DE genes are provided in AppendixB.

TABLE 9 Number of differentially expressed (DE) genes at a falsediscovery rate below 1% for each toxin exposure condition of interest.Number DE Genes Toxin Low Conc. High Conc. Arsenic 3 14 Cadmium 132 38Chromium(VI) 270 377 Cobalt 609 1274 Copper 110 77 Iron 32 70 Lead 99489 Malathion 370 41 Mercury 165 218 Nickel 43 95 Zinc 24 596

The resulting genes were cross inspected using different methods toconfirm that the most specific genes were selected. In order tosimultaneously analyze mean fold change with respect to the negativeconditions, the normalized counts, and the expression level with respectto other genes, we implemented a toolbox that depicts a summary figurefor a gene candidate as shown in FIG. 51 (see the full set of genecandidates in Appendix B).

From an analysis of all gene candidates, we set our mean fold changethreshold to indicate DE to 2.5. Our most sensitive and specific genecandidates for eight toxins identified via RNA-Seq analysis in E. coliMG1655 are shown in FIG. 1. The arsR promoter in FIG. 1A respondsspecifically and monotonically to increasing concentrations of arsenicalone and in combination with other heavy metals without exhibitingcrosstalk. This gene serves as an excellent positive control for ouranalysis methods, as our literature search revealed it to be the mostwidely studied gene for sensing arsenic in E. coli. The zntA promoter inFIG. 1B responds monotonically to increasing concentrations of cadmiumalone and in combination with other heavy metals without exhibitingcrosstalk, but it also shows sensitivity to low and high concentrationsof zinc. However, since the zraP promoter is specific for zinc, theboolean logic expression (zntA){circumflex over ( )}(:zraP) represents aunique combined promoter response that is specific for cadmium. The recNand sulA promoters in FIG. 1C-D respond specifically to highconcentrations of chromium(VI) alone. These promoters are known toactivate during multiple double-strand DNA breaks and the cell SOSresponse, respectively. Their differential expression here corroboratesrecent reports of chromium(VI) acting as a carcinogen in water suppliesand may provide information on its mode of action. The ygbA promoter inFIG. 1E responds specifically to cobalt alone. The cusR promoter in FIG.1F responds specifically to high concentrations of copper alone. Thisgene serves as another excellent positive control, as our literaturesearch showed it to be among the most widely studied genes for sensingcopper in E. coli. The ybiI promoter in FIG. 1G responds specificallyand monotonically to increasing concentrations of lead alone and showshigher specificity than the pbrR promoter identified from theliterature. The nemR promoter in FIG. 1H responds specifically to highconcentrations of malathion alone. The identification of a sensitive andspecific gene for detecting malathion in E. coli is entirely unexpected,as the mechanism of toxicity for organophosphate pesticides is known toonly operate in eukaryotes, as organophosphate pesticides areneurotoxins. Lastly, the zraP promoter in FIG. 1I responds specificallyand monotonically to increasing concentrations of zinc alone. Thesensitivity of zraP to zinc is extremely high, making this promoter anexcellent candidate for logical combination with promoters exhibitingzinc interference to generate toxin-specific multi-promoter responses.

We used RNA-Seq to verify the nasB promoter in B. subtilis 168 as asensitive and specific promoter for the last remaining toxin, ammonium(see FIG. 2). As opposed to other response circuitry, nasB isdown-regulated in response to the toxin, allowing the construction of a“lights-off” sensor. This is desirable because, unlike the other watertoxins of interest, ammonium is expected to increase the growth rate ofbiosensor cells. In this situation, a “lights-on” sensor could betriggered by any substance that increases the growth rate and resultingGFP production ability of cells, whereas a “lights-off” sensor remainsimmune to this effect.

In summary, through constructing strains based on literature searchesand performing RNA-Seq analysis, we discovered sensitive promoters forall toxins of interest. See Appendix A for a full list of candidatetoxin-responsive promoters.

Alternative 3

Milestone 3: Develop a preliminary microfluidic device that can culturemany independent E. coli sensor strains simultaneously

Library expansion of transcription based sensors

Microfluidic device development to support environmental sensing

Low cost optical methods development

Library of E. coli clones with target promoters producing GFP;preliminary microfluidic device capable of culturing many differentclonal populations in a defined array.

Library expansion of transcription based sensors. We have completed thismilestone. The promoter regions for the differentially expressed genesidentified in Milestone 2 were located using computational tools(Milestone 3.1). We have cloned these promoters into the Milestone 1expression systems (Milestone 3.2). We validated the functionality ofthese new promoter constructs using microfluidics (Milestone 3.3). Thisvalidation of our RNA-Seq identified promoters provides strong evidenceof the power and utility of our approach to developing noveltranscription based biosensors.

Milestone 3.1: Perform sequence analysis to determine the promoters ofcandidate genes identified in Milestone 2: We have identified thepromoter regions for all candidate genes. The promoters of interest arelocated on the genome directly upstream of the genes identified byRNASeq. For uncharacterized promoters, we used 200 bp upstream of thegene's transcription start site to construct the sensor plasmid.Including this entire region ensures that even cryptic regulatory sitesacting on the promoter are included in the sensor construct.

Milestone 3.2: Clone the promoters into the expression system validatedin Milestone 1: We have cloned all identified promoters into thestandardized plasmid expression system validated in Milestone 1.

Milestone 3.3: Validate the response of the promoters using the methodsdeveloped in Milestone 1: We validated our RNA-Seq approach toidentifying toxin-responsive promoters using plate reader data from fivecobalt-sensing promoters identified via RNA-Seq analysis (see FIG. 52).We grew strains in microplate wells in the presence of variousconcentrations of cobalt and measured GFP fluorescence. E. coli MG1655strains Co1 and Co2 incorporate the nmtR promoter from the M.tuberculosis genome as the cobalt-sensing element, whereas E. coliMG1655 strains Co3-Co8 incorporate cobalt-responsive promotersidentified through RNA-Seq. Odd numbered strains express GFP using thenative RBS, whereas even-numbered strains use the optimized Lutz RBS. Weconcluded that strains Co2-Co4 and Co6-Co8 significantly respond to thepresence of cobalt in the growth medium. Therefore, we have demonstratedthat our exhaustive methodology for RNA-Seq analysis supported bymultiple techniques and algorithms works.

The set of validated constructs shown in FIG. 3 contains one constructthat has been demonstrated sensitive to each toxin of interest withinour sensor prototype, with the exception of malathion for which thepositive results that we observed in our original microscopy runs (shownin this plot) have not yet translated to our sensor boxes. Duringinvestigation, we discovered a more robust malathion detection schemethat leverages its esterase inhibition effect in yeast (see FIG. 44).Our successful testing of this method on our microscope illustrates thepower of our forthcoming whole-genome detection scheme, whereby we willcontinually measure the responses of 2,000 to 4,000 E. coli and S.cerevisiae promoters arrayed in a microfluidic chip. We expect theobserved esterase inhibition to be significantly represented in thegenomic response of this multi-strain library during malathion exposure.

Milestone 4: Microfluidic device development to support environmentalsensing We developed a concentrated media additive to enable robustgrowth of E. coli in natural water sources (Milestone 4.1). We culturedE. coli in our microfluidic devices for up to 50 days, demonstratinglong-term reliability (Milestone 4.2). We developed and optimized amicrofluidics-capable peristaltic pump for extracting natural water(Milestone 4.3). We greatly expanded our microfluidic device, allowingthe culture of multiple strains (Milestone 4.4). We significantlyincreased the fluorescence signal from our microfluidically culturedcells by optimizing the trap geometry (Milestone 4.5). We developedtechniques to prevent the release of our genetically modified organisms(Milestone 4.6). We developed a reliable method to freeze-dry and storecells at room temperature (Milestone 4.7). We modified our “gill”microfluidic device to allow freeze-drying of cells on-chip (Milestone4.8). We successfully tested our microfluidic devices using our low-costoptical system (Milestone 4.9).

Milestone 4.1: Develop a concentrated media additive that can be mixedwith natural waters to support E. coli growth: We have developed andtested medium formulations that successfully culture E. coli and B.subtilis in batch and on-chip. For all batch culture experiments, M9minimal medium supplemented with 0.4% w/v glucose, 0.1 mM CaCl2, and 2mM MgSO4 was used. For B. subtilis, the medium was additionallysupplemented with 0.075% v/v TWEEN 20, 50 μM FeCl3, 50 μM MnCl2, and 1μM ZnCl2. For B. subtilis ammonium exposure experiments, the NH4Cl in M9minimal medium was replaced with NaNO3, keeping the concentration ofnitrogen constant.

For microfluidic experiments, we developed a minimal medium optimal forgrowth of bacteria and heavy metal sensing, adapted from HMM. Thismedium replaces the inorganic phosphate in M9 minimal medium withglycerol-2-phosphate, MOPS (pH=7.2), and KCl. Inorganic phosphate isundesirable because of its metal chelation properties and its propensityto form calcium phosphate deposits within microfluidic channels. Tominimize contaminating metals, all microfluidic experiments were carriedout with media made with extra high purity salts where available. Wefound that when using these pure salts robust E. coli growth requiredsupplementing the medium with iron, and robust B. subtilis growthrequired iron, zinc, and manganese.

The final composition of our E. coli medium, following on-chip mixingwith source water, was:

-   -   1. 40 mM PharmaGrade MOPS [Sigma #PHG0007-1KG] (from 1 M stock        at pH 7.2)    -   2. 4 mM glycerol-2-phosphate [Sigma #G6501-25G]    -   3. 0.4% w/v dextrose (glucose) [Sigma #D9434-1KG]    -   4. 1 g/l (19 mM or 262 ppm NH4-N) TraceSelect NH4Cl [Sigma        #09725-100G]    -   5. 3.7 g/l (50 mM) TraceSelect KCl [Sigma #05257-100G]    -   6. 0.075% v/v TWEEN 20 [Acros Organics #23336-0010]    -   7. 50 mg/mL spectinomycin (from spectinomycin dihydrochloride        pentahydrate) [Sigma #S4014-5G]    -   8. 1 μM FeCl3 [Alfa Aesar #A16231-500G]    -   9. 0.01 mM CaCl2 [Macron Fine Chemicals #4160-12]    -   10. 0.2 mM MgSO4 [Macron Fine Chemicals #6066-04]

The final composition of our B. subtilis medium, following on-chipmixing with source water, was the same as for E. coli with the followingmodifications:

1. Replace NH4Cl with 1.6 g/l NaNO3

2. Use 50 μM FeCl3 instead of 1 μM

3. Add 50 μM MnCl2 [Baker #2540-01]

4. Add 1 μM ZnCl2 [Macron #8780-04]

Note that TraceSelect formulations of reagents were used when availableto minimize the potential for heavy metal contamination of the media.

Milestone 4.2: Perform testing to demonstrate the long term culturestability of such cells. Quantify growth rate of cells and lifespan ofcultures: We have successfully grown cells in microfluidic devices witha stable growth rate comparable to batch culture.

We have continuously grown E. coli MG1655 in the gill chip for 23 daysusing growth medium concentrate mixed with natural water from LakeMiramar. In the most recent experiment, 10×M9 medium concentrate wasmixed with Milli-Q water in a 1:9 ratio at a total flow rate of 1 ml/husing a dual-channel Instech P625 peristaltic pump. Cells were observedto grow and express GFP in the traps after 50 days in the device,proving that the sensor strain is stable over this time period.

Milestone 4.3: Develop a metering and mixing system to combine theconcentrated media stock with natural water at a fixed ratio and mix itwell before cell exposure: We used an Instech P625 peristaltic pump tomix concentrated media with natural water at a fixed ratio andcalibrated the pump to achieve the desired flow rate. Since our mediaadditive stocks are concentrated 10-20×, we dilute them with naturalwater before delivery to the cells by using a peristaltic pump to driveeach liquid at a defined volumetric flow rate through silicone tubinginto the chip. Our pumping scheme uses a custom-made dual-channel tubingset with different tubing inner diameters, which results in a constantflow ratio. In laboratory tests, we have used tubing sets with 1:20 and1:9 flow ratios to successfully combine and completely mix the twoliquid streams on-chip using staggered herringbone mixers to supporthealthy cell growth.

Milestone 4.4: Expand the current E. coli large scale culture device(biopixel device) to have 500 individually addressable chambers: We havedesigned a large-scale gill chip using nested symmetrically-splitparallel channels to produce balanced flow (see FIG. 53). However, ouranalysis of RNASeq data determined that the incidence of specifictoxin-sensing promoters in E. coli is greater than expected. Therefore,we do not require hundreds of cell-trapping regions in the microfluidicdevice, and we decided to pursue a cell patterning strategy wherebycells are micropipetted into shallow but relatively large reservoirsupstream of the cell trapping regions.

Milestone 4.5: Increase the height of the Biopixel device's chambers toincrease the output optical signal. Ensure the cell growth dynamics areunchanged: We constructed and tested an optimized gill chip design thatraises the height of the cell trapping regions to 50 μm, therebyincreasing the fluorescent signal.

Notably, we have observed synthetic strains of E. coli MG1655 producelevels of GFP within the device that are visible to the naked eye usingthe appropriate filters. Over the course of dozens of experiments, wehave verified that cell growth rate is unaffected by these minormodifications to the “biopixel” cell trap design.

Milestone 4.6: Develop a UV LED system that kills the cells as theyemerge from the trapping region and enter waste collection. Performviability test on media exiting the waste trap: We concluded that UVLEDs are an inefficient method for sanitizing the chip effluentShort-wave UV LEDs consume power, emit heat, and have short lifetimes,which unnecessarily reduces the operational lifetime of our biosensordevice. Instead, we decided to route cell waste to a reservoircontaining bleach, which has been used as an effective sanitizer forhundreds of years.

We tested this strategy by depositing effluent from the pump mixingexperiments into a 1-l glass bottle pre-filled with 100 ml of bleach.The waste bottle was used to collect cell waste throughout fourexperiments over a period of two months and filled to around 500 ml. Totest for cell viability, the waste bottle was stirred, and 20 ml of thecontents was plated on LB agar. As a positive control, 45 ml of wastewas removed, bleached again, and plated using the same method. Neitherthe prebleached waste nor the post-bleached waste showed any bacterialgrowth after 1 day on LB agar without antibiotics, suggesting thepre-bleaching method is sufficient to eliminate viable cells in thewaste bottle.

Milestone 4.7: Develop a method to freeze-dry cells allowing them to berehydrated with little loss of viability: We have successfully developeda method for in chip lyophilization and revival after long term storage.Several cryoprotectants suitable for engineered biosensor strains wereformulated from a combination of literature-based protocols, currentindustrial practices, and experimentation. The investigatedcryoprotectants include:

1. 2.5% Luria-Bertrani Broth (LB) (w/v)+spectinomycin

2. 2.5% LB+0.4% glucose (w/v)+spectinomycin

3. 2.5% LB+0.4% sucrose (w/v)+spectinomycin

4. 2.5% LB+0.4% trehalose (w/v)+spectinomycin

5. M9+0.4% glucose+spectinomycin

Relative cryoprotectant efficacy was determined via plate reader revivalexperiments performed 24 h, 1 wk, 2 wk, 4 wk, and 8 wk afterlyophilization. Cells were revived via rehydration and resuspension in200 μl of revival medium within microplate wells. The plates were thenimmediately placed into a Tecan Infinite M200 Pro plate reader, wheregrowth rates were monitored over the next 48 h.

Revival media included:

1. M9+0.4% glucose+spectinomycin

2. Trace Select M9+0.4% glucose+spectinomycin

3. Trace Select M9+0.4% glucose

4. HM9 (nitrate)+0.4% glucose

and were selected to be representative of the growth medium used in thefinal device.

Strains protected with optimal cryoprotectants showed little differencein viability between cryoprotectants after two months of preservation.Both E. coli and B. subtilis strains responded similarly tolyophilization in the cryoprotectants listed above (see FIG. 4).

The best cryoprotectant was found to be LB+0.4% sucrose and was used totest batch and on-chip lyophilization and revival in biosensor hoststrains E. coli MG1655 and LABEC31 and B. subtilis NUB 3610. In batch,revival efficacy experiments were performed at 24 h, 1 wk, 2 wk, 4 wk,and 8 wk after lyophilization, with no observed reduction in viability.For on-chip testing, strains were cultured overnight to stationary andsporulation phases, respectively. Strains were then double-washed incryoprotectant and concentrated to 50× their batch culture concentrationbefore being injected into an 18-strain chip through independent loadingports. Loading ports were sealed with a fast-curing silicone elastomer(Sylgard 170, Dow Corning), then the device was lyophilized in acommercial freeze-dryer for 17 h before being nitrogen-flushed,desiccated, and sealed within opaque packaging.

Following room-temperature storage for up to two months, strains wererevived via de-gas driven chip wetting and pump driven flow. Uponintroducing fresh medium, strains revived on time scales equivalent tothose in batch (see FIG. 54). In addition, the ammonium sensing B.subtilis NCIB 3610 strain was successfully sporulated using standardsporulation medium, which offers an additional preservation method withextreme shelf life.

Milestone 4.8: Develop a deposition technique to place cells into aregion of a microfluidic device that is then bonded to a glasscoverslip: We have successfully developed a deposition strategy wherebyindividual biosensor strains are injected into on-chip reservoirs, wherethey are lyophilized, stored, and revived.

To independently culture multiple biosensor strains, an 18-strain chipwas designed, built, and successfully tested for multi-strain loadingand freeze-drying. The chip dimensions conform to the requirements ofboth the strains and the biosensor's optical detection system.

Milestone 4.9: Develop and test the microfluidic device in a laboratoryenvironment: We have conducted extensive testing of our microfluidicdevices in a laboratory environment, using both research grade and lowcost optical systems.

The microfluidic “gill” chip described in Milestone 1.4 was used to testthe optical system developed by the Ziva Corporation. We compared theoptical system to that of our research grade microscope, an OlympusIX81. The Ziva optical system was designed with lower resolution opticscompared to those of the 4× objective on the IX81 in order to lower costwhile increasing the imaged field of view. Although the Ziva optics arelower resolution, the produced images compare favorably with the IX81 asshown in FIG. 55.

Milestone 5: Low cost optical methods development Summary: We havesuccessfully completed this milestone. We have developed a low costoptical system by partnering with the Ziva Corporation, therebycompleting sub-milestones 5.1, 5.2, and 5.3. We have also determinedthat bioluminescent systems can be significantly more sensitive thanfluorescence based systems, thus completing Milestone 5.4.

Milestone 5.1: Design and construct an LED array for the GFP excitationof the cells in the microfluidic device: The excitation LED system hasbeen designed and delivered.

The optical setup was designed in partnership with the Ziva Corporation.We chose a CREE XTE Indus Star 1 Royal Blue High Power LED (manufacturerpart #CREEXTE-ROY-1) with an emission wavelength maximum of 465 nm,which is well suited for GFP excitation. The LED is paired with a 1400mA BuckBlock constant current LED driver (manufacturer part#0A009-DV-1400) and a LED light housing with a 15 W heat sink(manufacturer part #ALK-LH-15 W).

Milestone 5.2: Design and construct optical filters for GFP excitationand emission which cover the entire area of the microfluidic device: Theoptical filters for GFP excitation and emission have been designed anddelivered.

The optical setup was designed in partnership with the Ziva Corporation.The necessary filters were purchased from Semrock, Inc. The part numbersare FF495-Di03-25×36, FF01-520/35-23.3, and FF01-457/50-25 for thedichroic, emission, and excitation filters, respectively.

Milestone 5.3: Obtain and characterize the performance of a low costcamera system to image the fluorescence signal of the device: We havebuilt an imaging system using the “Chameleon” camera (part#CMLN-1252M-CS) from Point Grey Research, Inc. with the designassistance of the Ziva Corporation. This is a 1.3 megapixel monochromecamera featuring a Sony ICX445 CCD imager. It contains a 12-bitanalog-to-digital converter with a maximum gain of 24 dB. The camerapackage includes a software development kit (SDK), known as FlyCapture,which is compatible with the PandaBoard single board computer systemthat we have chosen for our electronics platform.

The microfluidic “gill” chip developed in Milestone 1.4 was used tocompare the optical system developed by the Ziva Corporation with ourresearch grade microscope, an Olympus IX81. The Ziva optical system wasdesigned with lower resolution optics compared to those of the 4×objective on the Olympus in order to lower cost while increasing theimage field of view by 20× for imaging multiple “gill” trapping regions.Images acquired with the Ziva optics compare favorably with thoseacquired with the Olympus, as shown in FIG. 55. Notably, the Ziva opticsare ˜2× more sensitive at detecting GFP than the Olympus optics (SNRsare 23.8 and 11.6, respectively). Because our primary objective is todetect weak signals, the Ziva system outperforms the Olympus system at˜50× lower cost.

Milestone 5.4: Replace the GFP fluorescence system with a luminescentsystem based on the lux operon of A. fischeri: We have replaced the GFPfluorescence system with a Lux system for our best-performing arsenicsensor plasmid.

The arsenic sensor construct As7 was modified to replace the gfp genewith the luxCDABE operon, and relative induction (fluorescence orluminescence) of the two constructs was tested side-by-side using aTecan Infinite M200 Pro plate reader (see FIG. 38). The background noisewas significantly lower for the Lux construct, allowing the detection ofarsenic at a concentration of 0.2 nM. In contrast, the GFP construct wassensitive to arsenic only above a concentration of 26 nM. Forcomparison, we were able to detect an arsenic concentration of 130 nMusing the GFP construct in a microfluidic device. Thus, replacing GFPwith Lux promises to greatly increase the sensitivity of the biosensor.

Task C

Task Objective: Develop a prototype of a deployable device for cheap andcontinuous monitoring of water contamination by specific targetcompounds

Metrics/Completion Criteria:

Milestone 6: Device prototype development

Milestone 7: Build computational models to determine the threshold ofdetection for specific sensors based on models of experimental GFPresponses

Milestone 8: Use the models obtained in Milestone 7 to investigatewhether the combination of nonspecific sensor responses to some toxinscan be utilized to improve specificity

Milestone 9: Develop the controller board to carry out sophisticatedanalysis of complex data

Deliverable: Finalized device prototype and computational algorithm forcontinuous water quality monitoring

Reporting Updates

Milestone 6: Device Prototype Development

We have successfully completed this milestone. We obtained andcharacterized a low power peristaltic pump (Milestone 6.1). We haveobtained a filter to prevent device clogging (Milestone 6.2). We havedemonstrated that flow reversal to prevent clogging is not necessarybased on our filtration design (Milestone 6.3). We have obtained anenvironmental enclosure and heater (Milestones 6.4 and 6.5). We haveobtained and developed the software components necessary for devicecontrol and data transmission (Milestone 6.6). We have developed apositive control stock for sensor testing purposes (Milestone 6.7). Wehave developed a solar cell based charging system (Milestone 6.8). Wehave assembled a fully functional prototype and have tested it in anoutdoor environment (Milestone 6.9).

Milestone 6.1: Find and characterize a low power pumping system that canpump natural water through the microfluidic device at a rate of 1 ml/h:We have purchased and tested a low power peristaltic pump compatiblewith microfluidics.

Milestone 6.2: Develop a filter system to prevent clogging of themicrofluidic device: We have successfully implemented filtering toprevent clogging when pumping natural water sources.

Milestone 6.3: Develop a flow reversal pumping regime to help clear thefilter of contaminants. Test and revise the de-clogging method to ensurea runtime of at least one month per sensor. Due to the success of ourfilter system developed in Milestone 6.2, we determined that flowreversal was not necessary.

Milestone 6.4: Develop a waterproof enclosure that can be used to housethe device and the electronics: We have purchased and tested anall-weather enclosure suitable for outdoor use.

Milestone 6.5: Develop a heating system to ensure the microfluidicdevice maintains the appropriate growth temperature: We have tested aheating system for our device.

Milestone 6.6: Develop the electronics/software to coordinate thepumping regimes, image capture, data transmission and device powermanagement: We have obtained the necessary hardware and software forcontrolling the electronic components of our biosensor. We assembledcomponents to support the time-lapse fluorescence imaging and analysisof cells within microfluidic devices. We also implemented a low cost,low power, all-solid-state PandaBoard single board computer to controlimage acquisition and peripherals. We implemented a fluorescence imagingassembly designed by the Ziva Corporation (FIG. 7D) to mount in ourprototype imaging scaffold. Light from a blue excitation LED reflectsoff a dichroic mirror and illuminates the sample. The optics collectfluorescent light emitted by the sample and pass it though the dichroicmirror and emission filter while focusing it to an image at the CCDcamera. We developed a custom software package to control dataacquisition, regulate pump speed, and maintain appropriate environmentalconditions for the biosensor.

Milestone 6.7: Develop a chemical control stock to test the properoperation of the biosensor (i.e. add low doses of toxins for positivecontrol): We developed concentrated chemical stocks of the eight toxinsthat are easily mixed to form a solution containing low doses of toxinsto serve as a positive control in testing proper biosensor operation.

Milestone 6.8: Develop a solar powered version of the prototype,including battery panels and charge controller): We have developed anindependent solar charging station that can easily interface with ourbiosensor device. To ensure compatibility with solar or battery power,we selected all components of our device (including the heater) to be DCpowered. We then located and purchased an isolated DC/DC converter (MeanWell part #SD-50A-12) that is capable of generating a regulated 12 VDCoutput from an unregulated 9.2-18 VDC input. The regulated 12 VDC supplyis used to power our pump, heater, and electronics. The unregulated9.2-18 VDC input is within the voltage range of a standard lead acidbattery; therefore, we purchased a deep cycle, lead acid battery(Interstate battery part #DCM0035) with sufficient energy storage (35Ah) to power our device for approximately two days. To charge thebattery, we purchased a 100 W monocrystalline solar panel and a 30 Apulse wave modulation (PWM) charge controller from Renology Solar. Thesolar panel components were assembled and mounted onto a galvanizedsteel pole at our field test facility.

Milestone 6.9: Develop a completed device prototype and test in anoutdoor environment): We have assembled the individual components listedin sub-milestones 6.1-6.8 into a functional prototype that is capable ofacquiring and processing data. Images of this prototype are shown inFIG. 7.

We have finalized the design of our 18-strain microfluidic device thatcan collect toxin response data from 18 different toxin specific strainssimultaneously (FIG. 56). This is housed inside our sensor prototype andimaged using our custom optics. The prototype is contained in a 16″_14″fiberglass enclosure (FIG. 7A) with an aluminum front panel designed toprotect the interior components from the environment. Aproportional-integral-derivative (PID) temperature controller is visiblein the upper left hand corner of the front panel with the peristalticwater pump in the lower right corner. The interior of the prototype isshown in FIG. 7B with the electronics sub enclosure (FIG. 7B-1) and Zivaoptical system clearly visible (FIG. 7B-5). The use of a PID controllerrather than a simple thermostat is necessary for precise control of theenclosure's internal temperature to within 0.1_C of the set point.Individual components are numbered 1-8. Briefly, the electronicsenclosure (FIG. 7B-1) contains the hardware for controlling the Zivaoptics, processing images, and transmitting data. This system can alsocommunicate with the temperature controller (FIG. 7B-2) to adjust theinterior temperature of the enclosure (normally kept at 37° C.) usingthe Modbus protocol.

This version of the prototype is designed to be powered from a 120 VACsource, and the AC power distribution block, along with a supplementalprotection circuit breaker and solid state relay for controlling theheater, is shown in FIG. 7B-3. A tri-voltage power supply, outputting 5,12, and 24 VDC for the electronics and pumps, is shown in FIG. 7B-4. TheZiva optical system for acquiring image data is shown in FIG. 7B-5. Theheater is combined with a circulating fan to distribute warm airthroughout the enclosure (FIG. 7B-6). Briefly, the user inputs thedesired temperature through a custom software package designed by ourgroup that runs on the PandaBoard system (FIG. 7B-1). The PandaBoardcommunicates with the PID controller (FIG. 7B-2) to set the desiredtemperature using the Modbus protocol. The PID controller modulates theheater's activity based on the set point temperature and the currenttemperature of the enclosure. To regulate the heater's output, the PIDcontroller generates a pulse wave signal that drives the activity of asolid state relay (FIG. 7B-3), which turns the heater's AC power sourceon and off (Note: the fan remains constantly on to circulate air). FIG.7B-7 shows the DC power distribution system for the electronics andperistaltic water pump (FIG. 7B-8).

To mount the necessary electronics for acquiring and processing data andto protect them from water exposure, we designed a custom sub-enclosureusing Solid-Works (Dassault Systems) and contracted its fabricationusing additive manufacturing (3-D printing) by a local machine shop(FIG. 7C). A depiction of the SolidWorks representation of thisenclosure, showing an Arduino Uno (FIG. 7C-1), BuckBlock LED drivers(FIG. 7C-2), PandaBoard system on a chip (FIG. 7C-3) and LED controlrelays (FIG. 7C-4) is shown in this Figure. The PandaBoard communicateswith the Arduino over a RS-232 (serial) link to modulate the LED controlrelays. The Arduino then generates a pulse wave modulated output signalthat is interpreted by the BuckBlock LED drivers to control thebrightness of the LEDs (one each for transmitted light and GFPexcitation). The PandaBoard is then responsible for acquiring an imagefrom the Ziva optical system (FIG. 7B-5) and finally signaling for theLEDs to be turned off. The acquired data is analyzed on the PandaBoard,and the results are transmitted via a secure Wi-Fi link to our datarepository server. The software to accomplish this was custom programmedin Java and C using the Point Grey FlyCapture SDK and implemented on aPandaBoard ES rev B.3 running Ubuntu Server. The various components ofthe Ziva optical system are shown in FIG. 7D, including the transmittedlight optics (FIG. 7D-1), the microfluidic device stage and thermistortemperature probe (FIG. 7D-2), the focus adjustment system (FIG. 7D-3),the dichroic mirror holder (FIG. 7D-4), the GFP excitation system (FIG.7D-5), and the Point Grey Chameleon monochrome camera (FIG. 7D-6). Animage of a microfluidic device, illuminated with the Ziva GFP excitationoptics is shown in FIG. 7E. We built five replicates of this completedprototype to run in our laboratory to collect data for the classifier.As proof of principle, we have also demonstrated that the biosensor canrun in an outdoor environment and draw a sample of raw water from apublic water source (FIG. 7F).

Milestone 7: Build Computational Models to Determine the Threshold ofDetection for Specific Sensors Based on Models of Experimental GFPResponses

Summary: We have successfully completed this milestone. We used machinelearning techniques to determine the relationships between the GFPoutput signal and the presence of a toxin (Milestone 7.1). We havecreated a database containing the collected sensor response data andhave incorporated all of the collected data (Milestone 7.2). We havequantified the GFP threshold of detection for each sensor construct(Milestone 7.3). We have constructed Receiver Operating Characteristic(ROC) curves for each sensor to achieve robust sensing (Milestone 7.4).

Milestone 7.1: Characterization of the family of GFP sensor responses tothe set of chemicals of interest via computational models): Weconstructed machine learning models capable of inferring therelationships between the GFP sensor responses and the presence orabsence of a toxin at a given concentration. The algorithm learns theserelationships from a set of training samples (GFP sensor responses)defined by the set of experimental conditions from which they weregenerated. The aim of the algorithm is to provide a general methodcapable of determining the experimental conditions associated with GFPsensor responses through the use of historical data.

Specifically, we have built classification models based on SupportVector Machines (SVMs), which is one of the most popular classifiers dueto its excellent performance in many contexts and its solid mathematicalbasis. For each toxin and concentration, we solved a binaryclassification problem in which the positive class represents thepresence of the toxin in water and the negative class is associated withclean environments. Patterns were constructed with features containingGFP sensor responses at various timestamps to capture the temporaldynamics of the GFP signal. The optimal meta-parameters of the SVMclassifier were determined by applying a 5-crossvalidation during thetraining phase.

In order to have a reliable estimate of the performance of the modelwhen deployed in real environments, we measured its performance over aset of samples (test patterns) not seen during the training phase. TheSVM's performance was determined by the percentage of test samplescorrectly labeled as toxin/no toxin (classification accuracy). 80% ofsamples were used for training the SVM models and the remaining 20% ofsamples was used to evaluate their effectiveness. We generated 20 randomtraining/test partitions based on data collected in single-strain chips(no crosstalk data) to have an estimate of performance. Table 10 showsthe average classification accuracy over the test set obtained acrossthe 20 random partitions for each binary classification problem.

TABLE 10 Classification accuracy results obtained from the GFP sensorresponses for multiple toxins at different concentrations Toxin Conc.(μM) Accuracy (%) Arsenic (pRS18) 0.2 97.35 0.55 100 1 100 Arsenic(pZA47a) 0.1 97.35 0.5 100 1 100 Cadmium 0.022 65.00 0.044 54.16 0.4490.00 Chromium(VI) 1.25 96.67 2.5 97.50 5 94.17 Copper 5 95.00 10 96.6720 95.00 Lead 1.8 95.83 3.6 95.00 7.2 95.00 Mercury 0.2 63.33 1 87.50 299.17 Ammonium 1 ppm 71.05 5 ppm 91.79

Milestone 7.2: Construction of a database of sensors' responses tochemicals of interest and null chemicals to be able to establish thestatistical significance in detection: We have constructed a databasethat is stored using MySQL in a Thecus NAS system capable of storing 22TB of data. All of the image sequences gathered from the five deviceprototypes have been organized into a directory structure for eachdevice and experiment. A master file containing the time stamp of eachimage and its file location has been compiled for quick image access viaa Network File System (NFS). After being processed forrotation/translation and feature extraction, these images are the inputvariables to the machine learning algorithms. The training data iscurrently stored using the following columns of information:

1. Date time

2. Date time in milliseconds since Unix epoch

3. Experiment ID

4. Device number

5. Toxin concentration at the start point

6. Concentration units at the start point

7. Toxin at the start point

8. Toxin at the start point using machine learning

label (see below)

9. Multiple toxin flag at the start point (are there multiple toxins?Y/N)

10. Toxin concentration at the end point

11. Concentration units at the end point

12. Toxin at the end point

13. Toxin at the end point using machine learning label

14. Multiple toxin flag at the end point

15. Responding strains

16. Additional notes

17. Delay time (time before the toxin(s) reach(es) the cells)

18. Success flag (did experiment complete successfully? Y/N)

The machine learning toxin labels are as follows: MilliQ ddH20=Code 1;Arsenic, As=2; Cadmium, Cd=3; Cobalt, Co=4; Chromium(VI), Cr=5; Copper,Cu=6; Mercury, Hg=7; Malathion, Mal=8; Lead, Pb=9; gfp tracer=10; andAmmonium, NH4-N=11.

Milestone 7.3: Quantification of the GFP threshold of detection withrespect to the concentration levels of the toxins): Based on the resultsof Milestone 7.1 (Table 10) and the ROC curves (FIG. 8), the modelsestablish the limits of detection that are described in Table 11.

TABLE 11 Toxin detection results for each toxin at variousconcentrations using a two-sample T-test at a 0.05 significance level.The first column is the actual concentration, the second column is theaverage estimated concentration, the third column is the standarddeviation of the estimation, and the fourth column is the result of thetest. Actual Conc. Estimated Std. Dev. Of T-Test Toxin (μM) Conc. (μM)Est. Conc. Result Arsenic 0.000000 0.094873 0.054253 FAIL 0.1000000.166705 0.039146 PASS 0.200000 0.121520 0.045036 PASS 0.500000 0.2692550.042667 PASS 1.000000 0.290522 0.029472 PASS Cadmium 0.000000 0.0736050.039364 FAIL 0.025000 0.084992 0.035848 PASS 0.050000 0.061783 0.019154FAIL 0.100000 0.081460 0.028670 PASS 0.440000 0.258721 0.054637 PASSChromium(VI) 0.000000 0.402180 0.211879 FAIL 0.200000 0.458967 0.162400PASS 0.500000 0.558889 0.163777 PASS 1.000000 0.816885 0.133032 PASS5.000000 1.215800 0.324680 PASS Copper 0.000000 1.037426 0.628584 FAIL0.100000 0.138169 0.120940 FAIL 0.500000 0.890369 0.184934 FAIL 1.0000001.428817 0.437622 PASS 2.000000 2.332876 0.320600 PASS 5.000000 1.8752580.285088 PASS 10.000000 2.931468 0.816670 PASS Lead 0.000000 0.6745180.526677 FAIL 0.600000 0.917133 0.294116 PASS 1.800000 1.636931 0.326718PASS 3.600000 2.284836 0.987410 PASS 7.200000 3.417676 1.085822 PASSMercury 0.000000 0.247743 0.185409 FAIL 0.100000 0.241039 0.102734 FAIL0.200000 0.245659 0.087578 FAIL 0.500000 0.392381 0.067889 PASS 2.0000000.968285 0.084330 PASS 6.000000 1.548434 0.286386 PASS

Toxin detection results at various concentrations were determined byusing a two-sample T-test at a 0.05 significance level. A nonlinearsupport vector regressor was used with the metaparameters crossvalidated using 5-fold cross-validation.

Milestone 7.4: Construction of the Receiver Operating Characteristiccurve (ROC) for the sensors to minimize false negatives and falsepositives): The results in Milestone 7.1 were obtained by assuming thatthe penalties of misclassification are identical for positive andnegative classes. In other words, the cost of classifying a GFP signalas “toxic” when it is not (or vice versa) is the same. However, it maybe the case that the cost is not symmetric for positive and negativecases. A water sensor is a good example of this situation, since itmight be preferable to ensure high accuracy when toxins are actually inthe water (true positive rate, TP) in exchange for increasing the numberof cases that are classified as “toxin present” when there is not anytoxin in the water (false positive rate, FP). The Receiver OperatingCharacteristic (ROC) curve is a 2-D parametrized curve used to quantifyand represent the tradeoff between the true positive rate and the falsepositive rate of a given classifier. The abscissa represents the FalsePositive rate, while the ordinate shows the True Positive rate.Therefore, the optimal classifier is represented by a point in the upperleft corner of the ROC curve, since this point corresponds to the bestpossible case in which the classifier is able to correctly identify 100%of positive cases (toxin present) with no false alarms.

The parameter that defines the ROC curve in our classification model isthe decision threshold, which determines whether a pattern (GFP signal)is classified as positive (toxin present) or negative (toxin notpresent). The SVM model provides a value (decision function) for eachpattern that represents the confidence of the model in its prediction,and the final classification is obtained by assigning to the negativeclass those points with decision functions that are below the decisionthreshold, and classifying as positive samples those patterns withdecision functions above this threshold. Therefore, by sweeping a gridof possible values for the SVM decision threshold, we obtained the ROCcurves for the various toxins shown in FIG. 8.

Milestone 8: Use the Models Obtained in Milestone 7 to InvestigateWhether the Combination of Nonspecific Sensor Responses to Some Toxinscan be Utilized to Improve Specificity

Summary: We have successfully completed this milestone. We havedemonstrated the high specificity of the strains we have developed(Milestone 8.1), and we have used state-of-the-art pattern recognitionalgorithms to improve the performance of the classifier (Milestone 8.2).

Milestone 8.1: Estimation of the number of nonspecific sensors requiredto achieve maximum specificity in the discrimination of the targetchemicals: Because we used RNA-Seq to find promoters that were highlychemically specific, we found there to be only a small amount ofcrosstalk. The combinatoric information provided by nonspecificresponses was used to strengthen classification performance. Todemonstrate this, we trained a classifier using a nonlinear supportvector machine on the GFP images concatenated to the estimations of thepixel derivatives for two different time scales. In Table 9 we presentthe confusion matrix of the sensor-specific strains to six differentchemicals at various concentrations.

TABLE 12 Confusion matrix solving a multiclass classification task forthe following classes: baseline, Arsenic, Cadmiun, Chromium(VI), Copper,Lead, and Mercury. Cobalt also performs well, but there is not enoughdata to provide proper statistics. The machine learning algorithm usedto calculate this table is a calibrated multiclass support vectormachine using 10-fold cross validation on the data. This matrix shouldbe read from left to right. For example, lead is confused with baselineactivity 6.4% of the time at low concentrations, while it rarely getsconfused with Mercury. The main diagonal indicates how often thealgorithms provide the right answer (e.g. 81.3% of the time for lead).None Arsenic Cadmium Chromium(VI) Copper Lead Mercury None 87.4% 2.0%2.6% 1.6% 0.8% 2.6% 3.0% Arsenic 6.2% 47.3% 7.4% 10.9% 9.3% 4.7% 14.3%Cadmium 6.7% 6.2% 73.3% 1.3% 4.6% 2.8% 5.1% Chromium(VI) 4.8% 10.6% 1.5%74.9% 3.4% 3.4% 1.3% Copper 2.1% 7.7% 4.5% 2.9% 76.5% 2.4% 3.9% Lead6.4% 2.9% 2.6% 2.7% 2.3% 81.3% 1.8% Mercury 6.2% 10.1% 4.0% 0.9% 3.1%1.5% 74.2%

The derivation of this matrix includes all of the availableconcentrations in the experiments, taking into account any non-specificresponses of the sensor-specific strains. Importantly, it demonstratesthat the classifier can discriminate between the toxins. That is, ineach row, there is one maximum number (i.e. the sensor responding to thecorrect toxin), and the rest of the numbers are uniformly low.

Generally, multi-class classification results nearing 80% represent ahighly successful algorithm, particularly given the novelty of thedevices and the data acquisition protocols. One issue contributing tonegative results is the way in which we probed the sensors. We initiallyexposed the sensors to higher levels of toxins to verify that we coulddetect responses. However, once we established that we could sense thesetoxins well, we dropped down to much lower levels near the detectionlimit for the majority of the subsequent inductions. This weighted ourdata heavily toward the realm of low responses and low signal, skewingour results to contain more errors. As we have only begun to probe theparameter space, we expect to strengthen the confusion matrixsignificantly as we move forward.

We have performed a similar analysis of our recent data collected usingthe 18-strain chip. This chip contains the new ammonium strain, whichprovides for a new confusion matrix, as seen in Table 13.

TABLE 13 Confusion matrix obtained from the 18-strain data. None ArsenicCadmium Chromium(VI) Cobalt Copper Lead Ammonium None 45.02% 7.66% 0.75%13.48% 9.69% 2.56% 5.26% 15.64% Arsenic 3.50% 92.58% 0.00% 0.14% 0.00%0.00% 0.00% 3.78% Cadmium 4.72% 0.00% 93.11% 2.17% 0.00% 0.00% 0.00%0.00% Chromium(VI) 8.05% 0.18% 0.25% 90.43% 0.11% 0.11% 0.52% 0.35%Cobalt 9.75% 0.00% 0.00% 0.21% 89.32% 0.49% 0.00% 0.22% Copper 15.73%0.00% 0.00% 1.12% 2.75% 78.27% 0.00% 2.14% Lead 7.71% 0.00% 0.00% 1.22%0.00% 0.00% 90.76% 0.31% Ammonium 10.94% 4.99% 0.00% 0.39% 0.12% 0.19%0.12% 83.30%

This is a confusion matrix generated by taking the average of tennon-overlapping partitions of the training set (80% of the data) and thetest set (20% of the data). Every training set of a partition wascross-validated to obtain the optimal metaparameters. After thecross-validation, the models were applied to the test set. We find thatammonium can be discriminated from the rest of the toxins 83% of thetime.

Milestone 8.2: Apply state of the art pattern recognition algorithmsusing the database built for Milestone 7 to improve performance of thedetection: We have employed nonlinear support vector machineclassification on images that have undergone two types oftransformation: 1) rotation and translation of the image with respect toa template for each of the sensor device setups (FIG. 57), and 2) anexponential moving average on the derivatives of the images to filterthe pixel noise.

We tried two different algorithms for image rotation and translation:minimization of the scalar product of the image with the templatereference image, and a normalized Euclidean distance from the brightfield image to the template. We found the Euclidean distance to be themost effective method. The algorithm can run in real time to track thevariations of the image's location in the device (FIG. 57). Once theimages have been aligned, we can apply a feature extraction algorithm tozoom in on the traps where the strains are located.

The feature extraction algorithm involves two phases: image reductionand derivative calculation. The image reduction phase applies asmoothing algorithm on neighboring pixels according to a circular kernelusing a radius of 41 pixels. The weights provided to the kernel have themaximum value in the center of the circle and decrease to a minimumvalue at the border of the circle. After the smoothing has been applied,the image is scaled from 720×640 pixels to 45×40 pixels. This operationreduces the size of the feature space significantly.

The feature extraction algorithm next calculates the derivatives of theimages to enhance the dynamic changes in the chip. The formula iscalculated as Δp^(K) _(ij)(t)=α^(K)ρ_(ij)(t)+(1−α)(1−p^(K) _(ij)(t−1)),and the time scales used in the filter are α¹=1/11, α²=1/26, andα³=1/51. In the end, the total number of features is (45×40)×4. In FIG.58 we show the original GFP image and the result of the derivativeestimation using three time scales (α¹, α², α³).

After the feature extraction algorithm has been applied, the classifieris trained using 80% of the data and tested using the remaining 20%. Weapply a 10-fold cross validation on the 80% of the training data todetermine the optimal model parameters before we test the model on theremaining 20%. The confusion matrices are then calculated by running anaverage on ten partitions of train and test. For the 8-strain chip data,the cross-validated classification accuracy obtained is 76% with astandard deviation of 2.57%. The results are summarized in the confusionmatrix (Table 12). Arsenic is the toxin that has the highest likelihoodof being confused with other toxins. This is due to collecting amajority of our data at very low arsenic levels to test the limits ofthe sensor, which skews the results. We have been continuouslycollecting data using five replicates of the sensor undergoing differentalignment and feature extraction algorithms. Overall, we are extremelysatisfied with the performance of this state-of-the-art classifier onthis dataset, particularly given the limited amount of data collectedthus far. Moreover, we have been intentionally probing the strains withconcentration levels that are difficult to detect and classify. As wecontinue to collect data with all 18 strains, the classifier willimprove further.

Milestone 9: Develop the Controller Board to Carry Out SophisticatedAnalysis of Complex Data

Summary: We have successfully completed this milestone. A PandaBoardsystem on a chip (SoC) was selected for the primary control system whichcontains integrated Wi-Fi (Milestone 9.1). We have developed advancedimage processing algorithms and embedded them on the PandaBoard(Milestone 9.2). We have developed algorithms which can easily be storedand loaded on our PandaBoard SoC (Milestone 9.3).

Milestone 9.1: Design the controller board with wireless capability: Wetested two low-power platforms based on the Texas Instruments ARMprocessor with wireless capability: one based on the Sitara ARM and theother based on the Cortex-A9. We have opted for the more powerfulCortex-A9 due to the ease of use and low power consumption. We arecurrently using a PandaBoard, which is powered by a Texas InstrumentsOMAP4430 system on a chip (SoC) device. The OMAP4430 chipset contains adual-core 1 GHz ARM Cortex-A9 MPCore CPU with 1 GB of DDR2 SDRAM, Wi-Ficapability, and an SD card slot offering up to 32 GB of storage. Theelectronics are similar to a modern smartphone in terms of processingpower and power consumption. The PandaBoard solution allows us toinstall a Linux operating system so that we can use standard GNUcompilers and run our software without any major modifications, which wedemonstrate in the next two aims.

Milestone 9.2: Reduce the size of the pattern recognition algorithms tobe able to be embedded in the PandaBoard. Fluorescence images are usedto train our classifier to be able to detect and discriminate betweendifferent toxins. In order to speed up the operation of the classifier,we must reduce its computational cost, which is directly linked to thenumber of images and the number of features in each image. Each imageconsists of a set of numerical features, each containing the intensityof a pixel in the image. Fortunately, the images contain many irrelevantfeatures and regions as well as a large number of redundant features inneighboring pixels. Therefore, it is extremely useful to apply a featureselection algorithm to find the most informative features and to reducethe computational cost of the classifier. In Milestone 8.2, we describethe image processing and feature selection process that enabled us toreduce computational cost and embed our algorithms in the PandaBoard.

Milestone 9.3: Embed the algorithms in the microcontroller. We havecompiled and executed nonlinear support vector machines 24 on the imagesdescribed in Milestone 9.2 after the feature selection process. Wecompiled the software, trained the model, and ran the trained modelwithout any problems. The algorithms were developed using the openMP APIand compiled using GNU g++. Both are well established and stable optionsthat run well on the PandaBoards. We implemented aggressive compileroptimizations to produce native code that runs fast on the multicoreCortex-A9 processors. Resulting performance was more than sufficient toallow real-time operation, with our embedded algorithms proving capableof classifying each image within 0.05 seconds.

As shown in FIG. 59 (FIGS. 59A-D), a single strain bank within thisdevice is shown in FIG. 59 panel A. Each strain is spotted within areservoir (1), where it expands via growth through feeder channels (2)into ten vertical trapping chambers (3). The cells proliferate inexponential phase within the trapping chambers due to the delivery offresh medium by convection through an adjacent channel (4) and diffusioninto the trapping chambers. Excess proliferating cells extend out of thetrapping chamber into the medium delivery channel, where they arecleared by convective flow. Trapping chambers remain densely packed dueto cross-seeding by neighboring gills via a linker channel (5). Theexponentially-growing culture in the trapping regions is imagedperiodically to measure the reporter response to various agentsintroduced through the medium delivery channel.

The cell trapping chambers are shown in detail in the FIG. 59B. Thechamber width is restricted to 10 μm to retain cells by limitingconvective flow. The maximum chamber length of 120 μm is restricted bythe delivery of nutrients to the rear of the trap via diffusion. Thechamber height of 50 μm was chosen to maximize photon collection alongthe vertical imaging axis while limiting the chamber aspect ratio tofacilitate fabrication using standard methods. The cell chamber geometryand quantity per bank may be optimized for various reporter strengthsand imaging sensitivities.

FIG. 59C shows a highly parallelized microfluidic device capable ofhousing 2,048 unique engineered strains. This embodiment of the deviceis divided into two strain arrays to allow simultaneous culturing andimaging of 1,024 reporter libraries of two microbial species. The pitchand format of the strain banks is compatible with spotting using arobotic pin tool or liquid handling instrument.

FIG. 59D shows a transmitted light image of an engineered strain growingexponentially within the trapping chambers of a single strain bank.

The format of the array of strain banks within our microfluidic deviceis shown in FIG. 60. The horizontal and vertical spotting pitch is 1.125mm, which is compatible with the well spacing of a 6,144-format SBSmicroplate. Our adherence to the 6,144-format microplate standard allowsus to easily spot up to 6,144 unique engineered strains within the85.48×127.76 mm footprint of a standard SBS microplate. The robotic toolused to spot strains into the cell reservoirs may be either (1) a pintool designed to transfer cells from solid or liquid medium or (2) aliquid handling tool designed to transfer cells from a liquid culture.

As shown in FIG. 60, in various embodiments, the microfluidic device maybe fabricated from PDMS, glass, and/or thermoplastics usingphotolithography, molding, etching, and/or embossing processes. Themicrofluidic device is assembled by sealing a monolith with recessedstrain banks and channels against a flat compatible material. Followingfabrication of the monolith, strains are robotically spotted into therecessed strain banks. Finally, the monolith is bonded to a flatmaterial using a compatible method such as thermal fusion, chemical orplasma surface activation, or the addition of an adhesive layer.

For the Sensor Strains, the pLB-Hg-i and pLB-Pb-i plasmids can also beused in some embodiments. These are not specifically sensor plasmids ontheir own, but can also be combined with any of our single-plasmidsensor strains, to make additional 2-plasmid sensor strains.

MORE EMBODIMENTS

In some embodiments, a microfluidic device comprising one or morecolonies or cultures of microorganism cells at one or more predeterminedaddressable locations, wherein each of the cells within the one or morecolonies or cultures comprises an expression cassette comprising abiosensor or promoter operably linked to a polynucleotide encoding adetectable agent, wherein transcription of the biosensor or promoter ismodulated by the presence of an analyte. In some embodiments, thedetectable agent is a nucleic acid, detectable protein, antibody-linkedreporter protein, enzymatic assay product, or electrochemical reactionproduct. In some embodiments, the detectable protein comprises anactivity that is increased or decreased in the presence of an analyte.In some embodiments, the detectable agent is a detectable protein,wherein the detectable protein provides a detectable signal. In someembodiments, the detectable protein is a fluorescent protein or aluminescent protein. In some embodiments, the nucleic acid is RNA orDNA. In some embodiments, the microfluidic device further comprisesmicrofluidic channels or lumens arranged in a rotationally symmetricgill cell trapping configuration. In some embodiments, the microfluidicchannels or lumens are arranged in 16 or 18 rotationally symmetricgills. In some embodiments, the device comprises about 20,000 chambersor gill traps. In some embodiments, transcription of the biosensor orpromoter is induced, promoted or increased by the presence of ananalyte. In some embodiments, wherein transcription of the biosensor orpromoter is induced, promoted or increased by the presence of an analyteselected from the group consisting of arsenic, cadmium, chromium VI,cobalt, copper, lead, malathion, mercury and zinc. In some embodiments,the biosensor or promoter is selected from the group consisting of ParsR(arsenic), PcadC (cadmium), PcadR (cadmium), PzntA (cadmium), PchrB(chromium VI), PchrS (chromium VI), PrecN (chromium VI), PsulA (chromiumVI), PumuD (chromium VI), PdadA (cobalt), Phmp (cobalt), PilvB (cobalt),PilvB (cobalt), PlipA (cobalt), PmmuP (cobalt), PnmtR (cobalt), PsoxR(cobalt), PtehA (cobalt), PygbA (cobalt), PyjbJ (cobalt), PyqfA(cobalt), PcopA (copper), PcusC (copper), PcusR (copper), PpbrR (lead),PmntH (lead), PshiA (lead), PybiI (lead), PyjjZ (lead), PcusC(malathion), PnemR (malathion), PmerR (mercury), PmntH (zinc), PshiA(zinc), PyjjZ (zinc), PzntA (zinc) and PzraP (zinc). In someembodiments, the biosensor or promoter comprises a polynucleotide havinga sequence identity of at least about 90% to a polynucleotide sequenceselected from the group consisting of SEQ ID NOs: 1-43. In someembodiments, the biosensor or promoter comprises a polynucleotide havinga sequence identity of at least about 90% to a polynucleotide sequenceselected from the group consisting of SEQ ID NOs: 2, 5, 8, 11, 12, 13,14, 15, 16, 17, 20, 23, 25, 28, 29, 30 and 33. In some embodiments,transcription of the biosensor or promoter is decreased or inhibited bythe presence of an analyte. In some embodiments, the biosensor orpromoter is decreased or inhibited by the presence of ammonia. In someembodiments, the biosensor or promoter is decreased or inhibited by thepresence of ammonia is selected from the group consisting of PnasA(ammonia), PnasB (ammonia), Pspo1-tnrA1 (ammonia) and Pspo1-tnrA2(ammonia). In some embodiments, the biosensor or promoter comprises apolynucleotide sequence having at least about 90% sequence identity toSEQ ID NO:1. In some embodiments, the device detects or monitors thepresence or levels of one or more analytes at the followingconcentrations: a) at least about 0.2 nM arsenic; b) at least about 0.44μM cadmium; c) at least about 2.5 μM chromium(VI); d) at least about 5μM copper; e) at least about 1 μM mercury; f) at least about 1.8 μMlead; g) at least about 72.5 mg/l malathion; and/or h) at least about 1ppm ammonia. In some embodiments, the microorganism cells are selectedfrom the group consisting of bacteria, cyanobacteria, microalgae andfungi. In some embodiments, the microorganism cells comprise a bacteriaselected from the group consisting of Escherichia coli, Bacillussubtilis, Salmonella sp., Aliivibrio fischeri, Pseudomonas fluorescens,Bacillus sp., Cupriavidus metallidurans, Deinococcus radiodurans, andStaphylococcus aureus. In some embodiments, the microorganism cellscomprise a fungus selected from the group consisting of Saccharomycescerevisiae and Trichosporon cutaneum. In some embodiments, themicroorganism cells comprise Synechocystis sp. In some embodiments, thedevice is capable of culturing at least about 4,000 individual strainsof microorganism cells. In some embodiments, the expression cassette isin a plasmid transformed into the microorganism. In some embodiments,the expression cassette is integrated into the genome of themicroorganism. In some embodiments, the one or more colonies or culturesof microorganisms are lyophilized (freeze-dried). In some embodiments,the one or more colonies or cultures of microorganisms are one or moredifferent species. In some embodiments, the one or more colonies orcultures of microorganisms are the same species. In some embodiments,the detectable protein is a fluorescent protein. In some embodiments,the fluorescent protein is selected from the group consisting of greenfluorescent protein, a yellow fluorescent protein, a cyan fluorescentprotein, a red-shifted green fluorescent protein (rs-GFP), and miniSOG.In some embodiments, the detectable protein is a luminescent protein. Insome embodiments, the luminescent protein is bacterial luciferase (Lux).In some embodiments, said microfluidic device comprises a plurality ofsaid colonies or cultures and wherein each of said plurality of coloniesor cultures comprises an expression cassette comprising a biosensor orpromoter operably linked to a polynucleotide encoding a detectable agentwherein transcription of the biosensor or promoter is modulated by thepresence of a different analyte than the biosensor or promoter in theother of said plurality of colonies or cultures. In some embodiments,the plurality of colonies or cultures comprises at least 2 colonies orcultures, 3 colonies or cultures, 4 colonies or cultures, 5 colonies orcultures, 6 colonies or cultures or 7 colonies or cultures. In someembodiments, the colonies or cultures comprise microorganism cells areselected from the group consisting of bacteria, cyanobacteria,microalgae and fungi. In some embodiments, the transcription of thebiosensor or promoter is induced, promoted or increased by the presenceof an analyte selected from the group consisting of arsenic, cadmium,chromium VI, cobalt, copper, lead, malathion, mercury and zinc. In someembodiments, the biosensor or promoter is selected from the groupconsisting of ParsR (arsenic), PcadC (cadmium), PcadR (cadmium), PzntA(cadmium), PchrB (chromium VI), PchrS (chromium VI), PrecN (chromiumVI), PsulA (chromium VI), PumuD (chromium VI), PdadA (cobalt), Phmp(cobalt), PilvB (cobalt), PilvB (cobalt), PlipA (cobalt), PmmuP(cobalt), PnmtR (cobalt), PsoxR (cobalt), PtehA (cobalt), PygbA(cobalt), PyjbJ (cobalt), PyqfA (cobalt), PcopA (copper), PcusC(copper), PcusR (copper), PpbrR (lead), PmntH (lead), PshiA (lead),PybiI (lead), PyjjZ (lead), PcusC (malathion), PnemR (malathion), PmerR(mercury), PmntH (zinc), PshiA (zinc), PyjjZ (zinc), PzntA (zinc) andPzraP (zinc).

In some embodiments, a system comprising the microfluidic device of anyone of the embodiments described herein, is provided. In someembodiments, the microfluidic device comprises one or more colonies orcultures of microorganism cells at one or more predetermined addressablelocations, wherein each of the cells within the one or more colonies orcultures comprises an expression cassette comprising a biosensor orpromoter operably linked to a polynucleotide encoding a detectableagent, wherein transcription of the biosensor or promoter is modulatedby the presence of an analyte. In some embodiments, the detectable agentis a nucleic acid, detectable protein, antibody-linked reporter protein,enzymatic assay product, or electrochemical reaction product. In someembodiments, the detectable protein comprises an activity that isincreased or decreased in the presence of an analyte. In someembodiments, the detectable agent is a detectable protein, wherein thedetectable protein provides a detectable signal. In some embodiments,the detectable protein is a fluorescent protein or a luminescentprotein. In some embodiments, the nucleic acid is RNA or DNA. In someembodiments, the microfluidic device further comprises microfluidicchannels or lumens arranged in a rotationally symmetric gill celltrapping configuration. In some embodiments, the microfluidic channelsor lumens are arranged in 16 or 18 rotationally symmetric gills. In someembodiments, the device comprises about 20,000 chambers or gill traps.In some embodiments, transcription of the biosensor or promoter isinduced, promoted or increased by the presence of an analyte. In someembodiments, wherein transcription of the biosensor or promoter isinduced, promoted or increased by the presence of an analyte selectedfrom the group consisting of arsenic, cadmium, chromium VI, cobalt,copper, lead, malathion, mercury and zinc. In some embodiments, thebiosensor or promoter is selected from the group consisting of ParsR(arsenic), PcadC (cadmium), PcadR (cadmium), PzntA (cadmium), PchrB(chromium VI), PchrS (chromium VI), PrecN (chromium VI), PsulA (chromiumVI), PumuD (chromium VI), PdadA (cobalt), Phmp (cobalt), PilvB (cobalt),PilvB (cobalt), PlipA (cobalt), PmmuP (cobalt), PnmtR (cobalt), PsoxR(cobalt), PtehA (cobalt), PygbA (cobalt), PyjbJ (cobalt), PyqfA(cobalt), PcopA (copper), PcusC (copper), PcusR (copper), PpbrR (lead),PmntH (lead), PshiA (lead), PybiI (lead), PyjjZ (lead), PcusC(malathion), PnemR (malathion), PmerR (mercury), PmntH (zinc), PshiA(zinc), PyjjZ (zinc), PzntA (zinc) and PzraP (zinc). In someembodiments, the biosensor or promoter comprises a polynucleotide havinga sequence identity of at least about 90% to a polynucleotide sequenceselected from the group consisting of SEQ ID NOs: 1-43. In someembodiments, the biosensor or promoter comprises a polynucleotide havinga sequence identity of at least about 90% to a polynucleotide sequenceselected from the group consisting of SEQ ID NOs: 2, 5, 8, 11, 12, 13,14, 15, 16, 17, 20, 23, 25, 28, 29, 30 and 33. In some embodiments,transcription of the biosensor or promoter is decreased or inhibited bythe presence of an analyte. In some embodiments, the biosensor orpromoter is decreased or inhibited by the presence of ammonia. In someembodiments, the biosensor or promoter is decreased or inhibited by thepresence of ammonia is selected from the group consisting of PnasA(ammonia), PnasB (ammonia), Pspo1-tnrA1 (ammonia) and Pspo1-tnrA2(ammonia). In some embodiments, the biosensor or promoter comprises apolynucleotide sequence having at least about 90% sequence identity toSEQ ID NO:1. In some embodiments, the device detects or monitors thepresence or levels of one or more analytes at the followingconcentrations: a) at least about 0.2 nM arsenic; b) at least about 0.44μM cadmium; c) at least about 2.5 μM chromium(VI); d) at least about 5μM copper; e) at least about 1 μM mercury; f) at least about 1.8 μMlead; g) at least about 72.5 mg/l malathion; and/or h) at least about 1ppm ammonia. In some embodiments, the microorganism cells are selectedfrom the group consisting of bacteria, cyanobacteria, microalgae andfungi. In some embodiments, the microorganism cells comprise a bacteriaselected from the group consisting of Escherichia coli, Bacillussubtilis, Salmonella sp., Aliivibrio fischeri, Pseudomonas fluorescens,Bacillus sp., Cupriavidus metallidurans, Deinococcus radiodurans, andStaphylococcus aureus. In some embodiments, the microorganism cellscomprise a fungus selected from the group consisting of Saccharomycescerevisiae and Trichosporon cutaneum. In some embodiments, themicroorganism cells comprise Synechocystis sp. In some embodiments, thedevice is capable of culturing at least about 4,000 individual strainsof microorganism cells. In some embodiments, the expression cassette isin a plasmid transformed into the microorganism. In some embodiments,the expression cassette is integrated into the genome of themicroorganism. In some embodiments, the one or more colonies or culturesof microorganisms are lyophilized (freeze-dried). In some embodiments,the one or more colonies or cultures of microorganisms are one or moredifferent species. In some embodiments, the one or more colonies orcultures of microorganisms are the same species. In some embodiments,the detectable protein is a fluorescent protein. In some embodiments,the fluorescent protein is selected from the group consisting of greenfluorescent protein, a yellow fluorescent protein, a cyan fluorescentprotein, a red-shifted green fluorescent protein (rs-GFP), and miniSOG.In some embodiments, the detectable protein is a luminescent protein. Insome embodiments, the luminescent protein is bacterial luciferase (Lux).In some embodiments, said microfluidic device comprises a plurality ofsaid colonies or cultures and wherein each of said plurality of coloniesor cultures comprises an expression cassette comprising a biosensor orpromoter operably linked to a polynucleotide encoding a detectable agentwherein transcription of the biosensor or promoter is modulated by thepresence of a different analyte than the biosensor or promoter in theother of said plurality of colonies or cultures. In some embodiments,the plurality of colonies or cultures comprises at least 2 colonies orcultures, 3 colonies or cultures, 4 colonies or cultures, 5 colonies orcultures, 6 colonies or cultures or 7 colonies or cultures. In someembodiments, the colonies or cultures comprise microorganism cells areselected from the group consisting of bacteria, cyanobacteria,microalgae and fungi. In some embodiments, the transcription of thebiosensor or promoter is induced, promoted or increased by the presenceof an analyte selected from the group consisting of arsenic, cadmium,chromium VI, cobalt, copper, lead, malathion, mercury and zinc. In someembodiments, the biosensor or promoter is selected from the groupconsisting of ParsR (arsenic), PcadC (cadmium), PcadR (cadmium), PzntA(cadmium), PchrB (chromium VI), PchrS (chromium VI), PrecN (chromiumVI), PsulA (chromium VI), PumuD (chromium VI), PdadA (cobalt), Phmp(cobalt), PilvB (cobalt), PilvB (cobalt), PlipA (cobalt), PmmuP(cobalt), PnmtR (cobalt), PsoxR (cobalt), PtehA (cobalt), PygbA(cobalt), PyjbJ (cobalt), PyqfA (cobalt), PcopA (copper), PcusC(copper), PcusR (copper), PpbrR (lead), PmntH (lead), PshiA (lead),PybiI (lead), PyjjZ (lead), PcusC (malathion), PnemR (malathion), PmerR(mercury), PmntH (zinc), PshiA (zinc), PyjjZ (zinc), PzntA (zinc) andPzraP (zinc). In some embodiments, the system further comprises ahousing enclosing the device, comprising within the housing: i) aperistaltic pump in fluid communication with the microfluidic device;ii) a fluorescent or luminescent signal sensor or detector comprising aplatform to accommodate the microfluidic device; and iii) electronicsfor acquiring and processing data in electronic communication with thefluorescent or luminescent signal sensor or detector. In someembodiments, the system is configured as depicted in FIG. 7. In someembodiments, the housing is temperature and/or humidity controlled.

In some embodiments, a method of detecting the presence or levels of ananalyte in an aqueous sample is provided, wherein the method comprisesa) inputting into the microfluidic lumens of a microfluidic device ofany one of the embodiments described herein, an aqueous test samplesuspected of comprising one or more analytes of interest such that theaqueous test sample contacts the one or more colonies or cultures ofmicroorganism cells; b) measuring the amount of a detectable agent thatcan correspond to a quantifiable level of analyte. In some embodiments,the detectable agent is a detectable protein, antibody-linked reporterprotein, enzymatic assay product, or electrochemical reaction productand the measuring comprises detecting the detectable protein,antibody-linked reporter protein, enzymatic assay product, orelectrochemical reaction product. In some embodiments, the detectableprotein is a fluorescent protein or a luminescent protein. In someembodiments, measuring comprises measuring the transcription and/oractivation levels of the detectable agent, wherein the transcriptionand/or activation levels of the detectable protein expressed by the oneor more colonies or cultures at the predetermined addressable locationscorrespond to a quantifiable level of analyte. In some embodiments, themethod further comprises measuring the fluorescence and/or theluminescence of the one or more detectable proteins expressed by the oneor more colonies or cultures at the predetermined addressable locationswithin the device.

In some embodiments, a collection is provided, wherein the collectioncomprises a plurality of different nucleic acids, wherein each nucleicacid within said collection comprises a first sequence comprising apromoter responsive to an analyte different from the analyte to whichthe other promoters in the other nucleic acids are responsive; and asecond sequence comprising a reporter protein. In some embodiments, thepromoter is selected from the group consisting of ParsR (arsenic), PcadC(cadmium), PcadR (cadmium), PzntA (cadmium), PchrB (chromium VI), PchrS(chromium VI), PrecN (chromium VI), PsulA (chromium VI), PumuD (chromiumVI), PdadA (cobalt), Phmp (cobalt), PilvB (cobalt), PilvB (cobalt),PlipA (cobalt), PmmuP (cobalt), PnmtR (cobalt), PsoxR (cobalt), PtehA(cobalt), PygbA (cobalt), PyjbJ (cobalt), PyqfA (cobalt), PcopA(copper), PcusC (copper), PcusR (copper), PpbrR (lead), PmntH (lead),PshiA (lead), PybiI (lead), PyjjZ (lead), PcusC (malathion), PnemR(malathion), PmerR (mercury), PmntH (zinc), PshiA (zinc), PyjjZ (zinc),PzntA (zinc) and PzraP (zinc). In some embodiments, the reporter proteinis a fluorescent protein or a luminescent protein. In some embodiments,the fluorescent protein is selected from the group consisting of greenfluorescent protein, a yellow fluorescent protein, a cyan fluorescentprotein, a red-shifted green fluorescent protein (rs-GFP), and miniSOG.

In some embodiments, a method of making a plurality of cell strains forthe detection of an analyte is provided, wherein the method comprisesintroducing into a plurality of cell strains the collection of anyone ofthe embodiments described herein. In some embodiments, the collectioncomprises a plurality of different nucleic acids, wherein each nucleicacid within said collection comprises a first sequence comprising apromoter responsive to an analyte different from the analyte to whichthe other promoters in the other nucleic acids are responsive; and asecond sequence comprising a reporter protein. In some embodiments, thepromoter is selected from the group consisting of ParsR (arsenic), PcadC(cadmium), PcadR (cadmium), PzntA (cadmium), PchrB (chromium VI), PchrS(chromium VI), PrecN (chromium VI), PsulA (chromium VI), PumuD (chromiumVI), PdadA (cobalt), Phmp (cobalt), PilvB (cobalt), PilvB (cobalt),PlipA (cobalt), PmmuP (cobalt), PnmtR (cobalt), PsoxR (cobalt), PtehA(cobalt), PygbA (cobalt), PyjbJ (cobalt), PyqfA (cobalt), PcopA(copper), PcusC (copper), PcusR (copper), PpbrR (lead), PmntH (lead),PshiA (lead), PybiI (lead), PyjjZ (lead), PcusC (malathion), PnemR(malathion), PmerR (mercury), PmntH (zinc), PshiA (zinc), PyjjZ (zinc),PzntA (zinc) and PzraP (zinc). In some embodiments, the reporter proteinis a fluorescent protein or a luminescent protein. In some embodiments,the fluorescent protein is selected from the group consisting of greenfluorescent protein, a yellow fluorescent protein, a cyan fluorescentprotein, a red-shifted green fluorescent protein (rs-GFP), and miniSOG.

In some embodiments, cell strains for the detection of an analyte areprovided. The cell strains can comprise the nucleic acid of anyone ofthe embodiments described herein, or can be made by the method of anyoneof the embodiments described herein. In some embodiments, the cell is ofbacteria, cyanobacteria, microalgae and fungi. In some embodiments, thebacteria is selected from the group consisting of Escherichia coli,Bacillus subtilis, Salmonella sp., Aliivibrio fischeri, Pseudomonasfluorescens, Bacillus sp., Cupriavidus metallidurans, Deinococcusradiodurans, and Staphylococcus aureus. In some embodiments, the cell isa fungus selected from the group consisting of Saccharomyces cerevisiaeand Trichosporon cutaneum. In some embodiments, the cell comprisesSynechocystis sp.

In some embodiments, a microfluidic device is provided. The microfluidicdevice can comprise a plurality of lyophilized cell strains wherein eachof said plurality of lyophilized cells strains has been geneticallyengineered to produce an increased or decreased amount of a detectableagent in the presence of an analyte relative to the amount produced inthe absence of said analyte. In some embodiments, the detectable agentis a nucleic acid, detectable protein, antibody-linked reporter protein,enzymatic assay product, or electrochemical reaction product. In someembodiments, the detectable protein is a fluorescent protein or aluminescent protein. In some embodiments, the detectable proteincomprises an activity that is increased or decreased in the presence ofan analyte. In some embodiments, the detectable agent is a detectableprotein, wherein the detectable protein provides a detectable signal. Insome embodiments, the nucleic acid is RNA or DNA. In some embodiments,the microfluidic channels or lumens are arranged in a rotationallysymmetric gill cell trapping configuration. In some embodiments, themicrofluidic channels or lumens are arranged in 16 or 18 rotationallysymmetric gills. In some embodiments, the device comprises about 20,000chambers or gill traps. In some embodiments, transcription of thebiosensor or promoter is induced, promoted or increased by the presenceof an analyte. In some embodiments, transcription of the biosensor orpromoter is induced, promoted or increased by the presence of an analyteselected from the group consisting of arsenic, cadmium, chromium VI,cobalt, copper, lead, malathion, mercury and zinc. In some embodiments,the biosensor or promoter is selected from the group consisting of ParsR(arsenic), PcadC (cadmium), PcadR (cadmium), PzntA (cadmium), PchrB(chromium VI), PchrS (chromium VI), PrecN (chromium VI), PsulA (chromiumVI), PumuD (chromium VI), PdadA (cobalt), Phmp (cobalt), PilvB (cobalt),PilvB (cobalt), PlipA (cobalt), PmmuP (cobalt), PnmtR (cobalt), PsoxR(cobalt), PtehA (cobalt), PygbA (cobalt), PyjbJ (cobalt), PyqfA(cobalt), PcopA (copper), PcusC (copper), PcusR (copper), PpbrR (lead),PmntH (lead), PshiA (lead), PybiI (lead), PyjjZ (lead), PcusC(malathion), PnemR (malathion), PmerR (mercury), PmntH (zinc), PshiA(zinc), PyjjZ (zinc), PzntA (zinc) and PzraP (zinc). In someembodiments, the biosensor or promoter comprises a polynucleotide havinga sequence identity of at least about 90% to a polynucleotide sequenceselected from the group consisting of SEQ ID NOs: 1-43. In someembodiments, the biosensor or promoter comprises a polynucleotide havinga sequence identity of at least about 90% to a polynucleotide sequenceselected from the group consisting of SEQ ID NOs: 2, 5, 8, 11, 12, 13,14, 15, 16, 17, 20, 23, 25, 28, 29, 30 and 33. In some embodiments,transcription of the biosensor or promoter is decreased or inhibited bythe presence of an analyte. In some embodiments, the biosensor orpromoter is decreased or inhibited by the presence of ammonia. In someembodiments, the biosensor or promoter which is decreased or inhibitedby the presence of ammonia is selected from the group consisting ofPnasA (ammonia), PnasB (ammonia), Pspo1-tnrA1 (ammonia) and Pspo1-tnrA2(ammonia). In some embodiments, the biosensor or promoter comprises apolynucleotide sequence having at least about 90% sequence identity toSEQ ID NO:1. In some embodiments, the device detects or monitors thepresence or levels of one or more analytes at the followingconcentrations: a) at least about 0.2 nM arsenic; b) at least about 0.44μM cadmium; c) at least about 2.5 μM chromium(VI); d) at least about 5μM copper; e) at least about 1 μM mercury; f) at least about 1.8 μMlead; g) at least about 72.5 mg/l malathion; and/or h) at least about 1ppm ammonia. In some embodiments, the microorganism cells are selectedfrom the group consisting of bacteria, cyanobacteria, microalgae andfungi. In some embodiments, the microorganism cells comprise a bacteriaselected from the group consisting of Escherichia coli, Bacillussubtilis, Salmonella sp., Aliivibrio fischeri, Pseudomonas fluorescens,Bacillus sp., Cupriavidus metallidurans, Deinococcus radiodurans, andStaphylococcus aureus. In some embodiments, the microorganism cellscomprise a fungus selected from the group consisting of Saccharomycescerevisiae and Trichosporon cutaneum. In some embodiments, themicroorganism cells comprise Synechocystis sp. In some embodiments, thedevice is capable of culturing at least about 4,000 individual strainsof microorganism cells. In some embodiments, the expression cassette isin a plasmid which has been introduced into the microorganism. In someembodiments, the expression cassette is integrated into the genome ofthe microorganism. In some embodiments, the one or more colonies orcultures of microorganisms are one or more different species. In someembodiments, the one or more colonies or cultures of microorganisms arethe same species. In some embodiments, the detectable protein is afluorescent protein. In some embodiments, the fluorescent protein isselected from the group consisting of green fluorescent protein, ayellow fluorescent protein, a cyan fluorescent protein, a red-shiftedgreen fluorescent protein (rs-GFP), and miniSOG. In some embodiments,the detectable protein is a luminescent protein. In some embodiments,the luminescent protein is bacterial luciferase (Lux).

APPENDIX A Candidate toxin-responsive promoters Concentration Sensed inSNR Microfluidic after Toxin Gene/Promoter Source RBS HostStrain/Plasmid Device (μM) 6 h Ammonia p_(nasA) B. subtilis genomenative Ammonia p_(nasA) B. subtilis genome synthetic Ammonia p_(nasB) B.subtilis genome native Ammonia p_(nasB) B. subtilis genome syntheticAmmonia P_(spo1)-_(tnrA1) B. subtilis genome synthetic AmmoniaP_(spo1)-_(tnrA2) B. subtilis genome synthetic Arsenic arsR/p_(arsR) E.coli plasmid native E. coli MG1655/As1 0.13 20 Arsenic arsR/p_(arsR) E.coli genome synthetic E. coli LABEC01/As3 Arsenic arsR/p_(arsR) E. coligenome synthetic E. coli MG1655/As3 0.13 33 Arsenic arsR/p_(arsR) S.aureus plasmid native E. coli MG1655/As5 Arsenic p_(arsR) E. coliRNA-Seq Cadmium cadC/p_(cadC) S. aureus plasmid native E. coliMG1655/Cd1 0.04 17 Cadmium cadC/p_(cadC) S. aureus plasmid synthetic E.coli MG1655/Cd2 Cadmium cadR/p_(cadR) P. putida genome native E. coliMG1655/Cd4 Cadmium cadR/p_(cadR) P. putida genome synthetic E. coliMG1655/Cd3 Cadmium p_(antA) E. coli RNA-Seq Chromium(VI) chrB/p_(chrB)C. metallidurans plasmid native E. coli MG1655/Cr3 Chromium(VI)chrB/p_(chrB) C. metallidurans plasmid synthetic E. coli MG1655/Cr2Chromium(VI) chrB/p_(chrB) O. tritici transposon native E. coliLABEC01/Cr5 5 5 Chromium(VI) chrB/p_(chrB) O. tritici transposon nativeE. coli MG1655/Cr5 Chromium(VI) chrB/p_(chrB) O. tritici transposonsynthetic E. coli LABEC01/Cr4 Chromium(VI) chrB/p_(chrB) O. triticitransposon synthetic E. coli MG1655/Cr4 Chromium(VI) chrS/p_(chrS) B.subtilis genome synthetic E. coli MG1655/Cr1 Chromium(VI) p_(recN) E.coli RNA-Seq Chromium(VI) p_(sulA) E. coli RNA-Seq Chromium(VI) p_(umuD)E. coli RNA-Seq Cobalt p_(dadA) E. coli RNA-Seq Cobalt p_(hmp) E. coliRNA-Seq Cobalt p_(ilvB) E. coli RNA-Seq Cobalt p_(lipA) E. coli RNA-SeqCobalt p_(mmuP) E. coli RNA-Seq native E. coli MG1655/Co7 Cobaltp_(mmuP) E. coli RNA-Seq synthetic E. coli MG1655/Co8 CobaltnmtR/p_(nmtR) M. tuberculosis genome native E. coli MG1655/Co1 CobaltnmtR/p_(nmtR) M. tuberculosis genome synthetic E. coli MG1655/Co2 Cobaltp_(soxR) E. coli RNA-Seq Cobalt p_(tehA) E. coli RNA-Seq Cobalt p_(ygbA)E. coli RNA-Seq native E. coli MG1655/Co3 Cobalt p_(ygbA) E. coliRNA-Seq synthetic E. coli MG1655/Co4 Cobalt p_(yjbJ) E. coli RNA-Seqnative E. coli MG1655/Co5 Cobalt p_(yjbJ) E. coli RNA-Seq synthetic E.coli MG1655/Co6 Cobalt p_(yqfA) E. coli RNA-Seq Copper cueR/p_(copA) E.coli genome native E. coli MG1655/Cu1 25 65 Copper (cusS/R)p_(cusC) E.coli genome native E. coli MG1655/Cu2 Copper p_(cusC) E. coli RNA-SeqCopper p_(cusR) E. coli RNA-Seq Lead pbrR/p_(pbrR) C. metalliduransplasmid native E. coli LABEC01/Pb1 Lead pbrR/p_(pbrR) C. metalliduransplasmid native E. coli MG1655/Pb1 Lead pbrR/p_(pbrR) C. metalliduransplasmid synthetic E. coli LABEC01/Pb2 7 18 Lead pbrR/p_(pbrR) C.metallidurans plasmid synthetic E. coli MG1655/Pb2 Lead p_(mntH) E. coliRNA-Seq Lead p_(shtA) E. coli RNA-Seq Lead p_(ybtI) E. coli RNA-Seq Leadp_(yjjz) E. coli RNA-Seq Malathion p_(cusC) E. coli RNA-Seq Malathionp_(nemR) E. coli RNA-Seq Mercury merR/p_(merR) E. coli plasmid native E.coli MG1655/Hg4 Mercury merR/p_(merR) E. coli plasmid synthetic E. coliMG1655/Hg3 0.1 20 Mercury merR/p_(merR) S. aureus plasmid native E. coliMG1655/Hg2 Mercury merR/p_(merR) S. aureus plasmid synthetic E. coliMG1655/Hgl Mercury merR/p_(merR) S. marcescens plasmid native E. coliMG1655/Hg6 Mercury merR/p_(merR) S. marcescens plasmid synthetic E. coliMG1655/Hg5 Zinc p_(mntH) E. coli RNA-Seq Zinc p_(shtA) E. coli RNA-SeqZinc p_(yjjz) E. coli RNA-Seq Zinc p_(zntA) E. coli RNA-Seq Zincp_(zraP) E. coli RNA-Seq

As shown in Appendix A, All candidate toxin-responsive promotersidentified in this work, ordered by the toxin of expected sensitivity.In the case of promoters identified by RNA-Seq, the gene is unknown. Forpromoters that have been expressed in a synthetic construct, theselected RBS and host strain are shown. If this synthetic construct hasbeen used to sense the toxin within a microfluidic device, theconcentration sensed and SNR after 6 h are shown.

Appendix B

RNA-Seq Results for Promoter Activation in E. coli MG1655 in Response toSingle and Multiple Toxin Exposures at Low and High Concentrations:

Our analysis of the sequencing data from RNA-Seq experiments todetermine candidate genes that are induced upon toxin exposure consistedof three main tasks: sequence alignment, quantification of geneexpression, and identification of differentially expressed genes.

Sequence alignment: Reads were aligned to the reference E. coli K-12substr. MG1655 genome using a tolerance of at most two mismatches peralignment to protect against sequencing errors. The alignment wasperformed using Bowtie software, 27 which is known to be very efficientin aligning reads to a reference genome.

Quantification of gene expression: The expression level of each gene wasdetermined as a function of the number of aligned reads mapping to thegene. After analyzing several approaches adopted in the literature totabulate the number of reads mapping to each gene, we implemented ourown software capable of reproducing the counting algorithms behind someof the standard toolboxes such as Bedtools28 and HTSeq.29 In particular,we counted the number of reads mapping to each gene regardless ofwhether the read mapped to several genes, taking into account thestrand-specificity of each read. Additionally, we implemented our ownalgorithms for sequence alignment and quantification of gene expressionin order to crosscheck all results.

Identification of differentially expressed genes: Finally, a set ofstatistical and information theory algorithms were applied in order toextract not only differentially expressed (DE) genes for each toxin withrespect to the control samples (pure water) but also toxin-specificgenes. DESeq is a standard tool for identifying DE genes that allowed usto select sensitive genes with differential expression between thecontrol samples (pure water) and the cells exposed to toxin. It assumesthat the number of counts for each gene across experimental replicatesfollows a negative binomial distribution.30, 31 We considered genes witha False Discovery Rate (FDR) lower than 1% as DE in order to ensurestatistically robust DE genes. We note that some genes showed highvariability in the control samples across different batches of RNA-Seqexperiments, indicating that these genes are very sensitive toenvironmental conditions. We identified 846 of these genes by performinga DESeq differential analysis (FDR<1%) between the control samples indifferent batches and subsequently removed them from the candidate pool.The number of DE genes (FDR<1%) identified for each condition whencompared to the negative samples in the same batch and after removinggenes that are DE between control samples is given in Table 9.

Ideally, good candidate specific genes are those with a significantfold-change with respect to the control samples but with a negligiblefold-change with respect to the other toxins. Additionally, genes withthe largest number of counts and expression levels are preferable inorder to maximize the signal-to-noise ratio. When it is not possible tofind toxin-specific genes, the next generation of good candidates isformed by those genes satisfying the above properties for a small subsetof toxins (multiple-toxin response). It is desirable to havesingle-toxin-specific genes for several of the toxins in the combinationin order to determine toxin-specific multi-gene-responses by means oflogical operations.

In rare cases, shared genes are differentially expressed. Therefore, wehave developed information theoretic measures to improve the toxinseparability. The core idea of the approach is that low entropies (orhighly informative genes) correspond to toxin-specific genes, whilelarge entropies (low information) are associated with scenarios in whichDE fold-changes across different toxins are similar and should bediscarded. The result of the analysis shows that toxins can easily bediscriminated by using simple boolean rules as shown in the main report.

All references listed herein are incorporated herein by reference intheir entireties, including the following references:

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It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. All publications, patents, and patentapplications cited herein are hereby incorporated by reference in theirentirety for all purposes.

1.-89. (canceled)
 90. A plurality of recombinant microbial cell strains,each strain comprising a different expression cassette comprising abiosensor or promoter operably linked to a polynucleotide encoding adetectable agent, wherein transcription of the biosensor or promoter ismodulated by the presence of a heavy metal analyte.
 91. The plurality ofrecombinant microbial cell strains of claim 90, wherein the heavy metalanalyte is selected from the group consisting of arsenic, cadmium,chromium VI, cobalt, copper, lead, mercury, and zinc.
 92. The pluralityof recombinant microbial cell strains of claim 90, wherein the biosensoror promoter is selected from the group consisting of ParsR (arsenic),PcadC (cadmium), PcadR (cadmium), PzntA (cadmium), PchrB (chromium VI),PchrS (chromium VI), PrecN (chromium VI), PsulA (chromium VI), PumuD(chromium VI), PdadA (cobalt), Phmp (cobalt), PilvB (cobalt), PlipA(cobalt), PmmuP (cobalt), PnmtR (cobalt), PsoxR (cobalt), PtehA(cobalt), PygbA (cobalt), PyjbJ (cobalt), PyqfA (cobalt), PcopA(copper), PcusC (copper), PcusR (copper), PpbrR (lead), PmntH (lead),PshiA (lead), PybiI (lead), PyjjZ (lead), PmerR (mercury), PmntH (zinc),PshiA (zinc), PyjjZ (zinc), PzntA (zinc), and PzraP (zinc).
 93. Theplurality of recombinant microbial cell strains of claim 90, wherein thedetectable agent is a nucleic acid, detectable protein, antibody-linkedreporter protein, enzymatic assay product, or electrochemical reactionproduct.
 94. The plurality of recombinant microbial cell strains ofclaim 90, wherein the detectable agent is a fluorescent protein or aluminescent protein.
 95. The plurality of recombinant microbial cellstrains of claim 90, wherein the microbial cell is a bacterium,cyanobacterium, a fungus, a microalga, or an alga.
 96. The plurality ofrecombinant microbial cell strains of claim 95, wherein the bacterium isselected from the group consisting of Escherichia coli, Bacillussubtilis, Salmonella sp., Aliivibrio fischeri, Pseudomonas fluorescens,Bacillus sp., Cupriavidus metallidurans, Deinococcus radiodurans, andStaphylococcus aureus.
 97. The plurality of recombinant microbial cellstrains of claim 95, wherein the fungus is selected from the groupconsisting of Saccharomyces cerevisiae and Trichosporon cutaneum. 98.The plurality of recombinant microbial cell strains of claim 95, whereinthe cyanobacterium is Synechocystis sp.
 99. A method of detecting thepresence of a heavy metal analyte of interest, comprising: (a)culturing, in a defined array, the plurality of recombinant microbialcell strains of claim 90, each strain of the plurality of recombinantmicrobial cell strains, comprising a different expression cassettecomprising a biosensor or promoter operably linked to a polynucleotideencoding a detectable agent, wherein transcription of the biosensor orpromoter is modulated by the presence of a heavy metal analyte ofinterest; (b) delivering an aqueous sample suspected of comprising theheavy metal analyte of interest to each strain of the plurality ofrecombinant microbial cell strains in the defined array underenvironmental conditions permitting modulated expression of thedetectable agent in the presence of the heavy metal analyte of interest;(c) measuring in the defined array, an amount of the detectable agent,corresponding to a quantifiable level of the heavy metal analyte ofinterest; and (d) quantifying the quantifiable level of the heavy metalanalyte of interest in the aqueous sample corresponding to the amount ofthe detectable agent measured in step (c).
 100. The method of claim 99,wherein the heavy metal analyte is selected from the group consisting ofarsenic, cadmium, chromium VI, cobalt, copper, lead, mercury, and zinc,or is more than one of these members.
 101. The method of claim 99,wherein the biosensor or promoter is selected from the group consistingof ParsR (arsenic), PcadC (cadmium), PcadR (cadmium), PzntA (cadmium),PchrB (chromium VI), PchrS (chromium VI), PrecN (chromium VI), PsulA(chromium VI), PumuD (chromium VI), PdadA (cobalt), Phmp (cobalt), PilvB(cobalt), PlipA (cobalt), PmmuP (cobalt), PnmtR (cobalt), PsoxR(cobalt), PtehA (cobalt), PygbA (cobalt), PyjbJ (cobalt), PyqfA(cobalt), PcopA (copper), PcusC (copper), PcusR (copper), PpbrR (lead),PmntH (lead), PshiA (lead), PybiI (lead), PyjjZ (lead), PmerR (mercury),PmntH (zinc), PshiA (zinc), PyjjZ (zinc), PzntA (zinc) and PzraP (zinc).102. The method of claim 99, wherein the detectable agent is a nucleicacid, detectable protein, antibody-linked reporter protein, enzymaticassay product, or electrochemical reaction product.
 103. The method ofclaim 99, wherein the detectable agent is a fluorescent protein or aluminescent protein.
 104. The method of claim 99, wherein the microbialcell is a bacterium, cyanobacterium, a fungus, a microalga, or an alga.105. The method of claim 104, wherein the bacterium is selected from thegroup consisting of Escherichia coli, Bacillus subtilis, Salmonella sp.,Aliivibrio fischeri, Pseudomonas fluorescens, Bacillus sp., Cupriavidusmetallidurans, Deinococcus radiodurans, and Staphylococcus aureus. 106.The method of claim 104, wherein the fungus is selected from the groupconsisting of Saccharomyces cerevisiae and Trichosporon cutaneum. 107.The method of claim 104, wherein the cyanobacterium is Synechocystis sp.108. The method of claim 99, wherein step (c) comprises measuring atranscription level and/or an activation level of the detectable agent,wherein the transcription level and/or the activation level is expressedby a strain of the plurality of recombinant microbial cell strains, inthe defined array.
 109. The method of claim 108, wherein thetranscription level and/or the activation level of the detectable agentcorresponds to the quantifiable level of the heavy metal analyte ofinterest.
 110. The method of claim 99, wherein step (c) comprisesmeasuring a fluorescence and/or a luminescence of the detectable agent.